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Showing posts with label Forensic economics. Show all posts
Showing posts with label Forensic economics. Show all posts
Context and Objective: The occurrence of
suspected cases of abuse against older adults is a global phenomenon;
health professionals must pay close attention to the possibility of
abuse against this population. This study aimed to describe the profile
of notifications of abuse against older adults reported in SINAN (the
official database of the Brazilian Ministry of Health) between 2009 and
July 2014.
Design: Cross-sectional study.
Setting: The manuscript was produced in the
Department of Legal Medicine of the Medical School of Universidade de
São Paulo and ABC Faculty of Medicine.
Methods: Data on domestic, sexual, and other
forms of violence were obtained from the Ministry of Health’s official
website (Sistema de Informação e Agravos de Notification – SINAN)
between 2009 and July 2014 (http://dtr2004.saude.gov.br/
sinanweb/index.php). The selected subjects were elderly aged 60 years or
older of both sexes.
Results: The number of notifications showed a
proportional increase between 2009 and mid-July 2014; most of the
attacks occurred in the victims’ homes, and the most frequent aggressors
were sons or daughters, followed by unknown people and acquaintances.
Women were the most attacked. Many aggressions were recurrent and in
multiple forms, but physical aggression and negligence were the most
reported.
Conclusion: This study’s great importance is
exposing the quantitative data of this violent scenario so that health
professionals who care for older adults remain alert to the
possibilities of mistreatment and that they notify suspected or proven
cases, as determined by Brazilian law.
With the global growth of the elderly population,
specific issues related to this group of individuals should be widely
discussed. Among these issues is violence, which is essential because
mistreatment of older people is associated with distress and increased
mortality [1]. Therefore, it is a serious public health problem.
Abuse is defined as violating an individual’s human
and civil rights. The abuse might consist of a single or repeated act,
often affecting vulnerable people and occurring in any relationship. The
following six types of abuse have been described: physical, sexual,
psychological, financial, or material, neglect or act of omission, and
discriminatory [2]. According to the Brazilian Institute of Geography
and Statistics (IBGE) [3], in 1950, older adults comprised 4% of the
country’s total population; in 1991, they represented 7.3%; 8.6% in 2000
(14.5 million) [4]; and 10% in 2010. Thus, according to the World
Health Organization (WHO), Brazil is experiencing a rapid increase in
the number and percentage of older people, often within a single
generation, and needs to adjust to this new reality [5]. Brazilian laws
are
strict regarding the notification of confirmed or suspected cases
of abuse against older people. Law nº. 12.461 (2011), amended
Article 19 of the Statute of the Elderly (Law nº. 10.741/2003),
is more rigorous than the previous law. It mandates that the
notification of abuse against older adults be compulsory, even in
suspected cases, and should be performed by health authorities
through private and public health services and any official agency
(Police authority; Public Ministry; Municipal Council for the
Elderly, the State Council of the Elderly, and the National Council
for the Elderly) [5,6].
Concerning the sub notification, Cooper et al. [1] in a
systematic review published in 2008, concluded that 6% of
older people reported significant abuse in the last month, 5.6%
of couples reported physical violence in their relationship in the
last year, and one in four vulnerable elders are at risk of abuse;
however, only a tiny proportion of this abuse is currently detected.
In Brazil, according to the Information System of Aggravation of
Notification (SINAN Net), which is associated with the Brazilian
Ministry of Health, there were significant fluctuations in the total
numbers of specific notifications of abuse and violence-related
deaths among people over age 60 between 2009 and 2011 [7].
In 2009, there were 1748 notifications of abuse, 2.5% of which
resulted in death; 3298 notifications of abuse, with 1.17% of
these resulting in death, occurred in 2010, and 5307 notifications
of abuse, with 4.6% of these cases resulting in death, occurred
in 2011 [6,7]. Mascarenhas et al. [7] evaluated many aspects
of the reports of violence against older adults in the Brazilian
Health Services [7,8]. These authors described the victims’ profile
as Caucasian males, between 60 and 69 years of age, single
or widowed, with low education levels, and with no physical
disabilities or mental or behavioral disorders. Physical violence
was prevalent among men, whereas psychological violence, sexual
violence, and neglect were more prevalent among older women
[7,8]. Another Brazilian study showed that women over 75 years
of age, single or widowed, with low education levels and chronic
diseases, are primary abuse victims [8,9]. This data will facilitate
the assessment of the primary at-risk groups suffering from
violence and provide information for establishing public policies
to reduce the number of abused elderly individuals. Thus, medical
physicians and forensic examiners should recognize these signs to
identify cases of abuse against older adults and avoid recurrences
in non-fatal violence and vengeful perpetrators.
This study aimed to describe the profile of notifications of
abuse against older adults reported in SINAN (the official database
of the Health Ministry of Brazil) from 2009 to July 2014.
The results of this cross-sectional study were conducted
through analysis of data published on the official website of the
Brazilian Ministry of Health: SINAN Net. (http://dtr2004.saude.
gov.br/sinanweb/). The information in this database included
notifications of violence (domestic, sexual, and other types of
violence against the older adults), which were included in the list
of compulsory notifications of violence according to the Ordinance
GM/MS Nº 104, January 25, 2011. We analyzed the notifications
from 2009 to July 2014. The study parameters included the sex and
age of the victim, the victim’s relationship to the perpetrator, the
place of the aggression, and the type and form of the aggression.
The results were compared with data from the literature.
In the period from January 2009 to July 2014, the SINAN
(official database of the Brazilian Ministry of Health) received
655,720 compulsory notifications of violence, and 35,155 of these
cases involved the older adults population (over 60 years of age),
representing 5.36% of the total.
Setting of the aggression, sex, and ethnicity of the
victims
68% of these cases (24,167) occurred in the victim’s residence,
and were reported 12,314 cases recurrent violence, which
resulted in 1111 deaths. The women were the most frequent
victims, accounting for 54% of total aggressions (maintaining
the ratio above 50% for all study years), ethnicity was Caucasian
(Graph 1), and 33,3% had low levels of education.
In the analyzed period, the average percentage of elderly
victims of total violence remained stable, at around 5.14%,
compared to the total violent notifications (all ages), as shown in
Table 1.
The physical violence and deaths
However, from 2009 to July 2014, notification of physical
violence against older people increases in Brazil (Graph 2). The
partial number of notifications until July exceeded the total
number in 2011. In 2010 there was an increase of 103% compared
to the previous year; in 2011, the increase compared to 2010 was
58,7%, and 68,44% in 2011 compared to 2012. However, there
was a slowdown in 2013 when the increase in notification was
14,69% concerning 2012. The total of notifications of physical
abuse was 23,542 in the period (2009 to July 2014), with 1111
deaths (Graph 3); 14,000 cases were caused by beating.
Physical abuse was more frequent in men (51,5%) than
women, but the difference was irrelevant. The perpetrators
were sons or daughters in 4,305 notifications. A stranger was
the aggressor in 3,304 cases; friends were 2,696, and husband or
ex-husband, the number of notifications, was 2,478. In the 2,558
aggressions, the instrument used was the knife.
Neglect, abandonment, and sexual violence
Neglect and abandonment were the second most frequent
kind of violence against older people. In the period of this study,
9,296 notifications were performed, with 129 deaths. Women
were the preferred victims, 4,872 cases (52,4%). Table 2 shows
the notifications according to race. There were 9,241 cases of
sexual and psychomoral violence reported during the period, of
which 6,595 were women (71.36%). Considering only reports of
isolated sexual violence, there were 782 cases, 94.91% of which
involved women.
Our study showed that 5.36% of the total notifications
from January 2009 to July 2014 were related to older people.
A Portuguese study applied between January and June 2013
demonstrated that 23.5% of older adults have suffered some
abuse, emotional and neglect especially; these results were
obtained through a questionnaire [10], which may have facilitated
the complaint. In Brazil, in 2013, notifications of violence against
the older population were 5.7%. According to the WHO, 36%
of American nursing home staff reported witnessing abuse in
the past year, with 10% witnessing physical abuse and 40%
witnessing psychological abuse.9,10 These data do not show the
true scope of this type of crime, as most abusers are close relatives
(i.e., sons and daughters). Another study (a systematic review of
the prevalence of abused elderly individuals) showed that the
prevalence of abused elderly individuals varies according to
culture and the definition and measure of violence. According to
these authors, 25% of older adults are at risk of abuse, although
few cases have been identified1. Many forms of violence are
imposed against older people, as these victims are vulnerable
individuals who are often unable to react to attacks or depend
on the abuser. In some situations, an old individual has difficulty
understanding the violent act or does not want to report the abuser
due to their relationship. The term “abuse against the elderly”
has often been associated with physical aggression; however,
neglect and emotional abuse (psychological) play essential roles
[9,11]. Few studies consider neglect as a separate form of abuse.
The definition of abuse against the elderly is complex, involves
multiples phenomena, and varies considerably among researchers
and between the laws of different countries, even among the states
of a country, as in the U.S. Concerning determining the age limit
of elderly individuals, some studies suggest ages above 60 years.
In contrast, others only consider ages over 65 years. This varying
definition is a problem that affects epidemiological studies of
the incidence of these events. According to Akaza et al. (2010),
other confounding factors involve the different classifications of
the types of maltreatment (violence). For some authors, violence
is classified into four types (physical, emotional, financial, and
neglect). In contrast, others add self-neglect, sexual abuse, and
other miscellaneous forms of abuse [11,12]. The prevalence of
elderly abuse is difficult to determine, as some studies define older
adults as over 60. In contrast, other studies consider people over
65 years. Although all people can be battered, the most frequent
victims are vulnerable individuals, such as children, teenagers,
women, homosexuals, people with disabilities or mental disorders,
and the elderly [8,11]. The Ministry of Health warns that falling
accidents among the elderly and their consequences have become
an epidemic in Brazil, with more than 20,000 hospitalizations in
2009. The Brazilian government recommends that all physicians
who evaluate elderly individuals suffering from falls (accidental or
not) ask these individuals whether there were any other injuries
or falls in the last six months [12,13].
In 2007, according to the Ministry of Health, 18,946 older
adults died from external causes, representing the seventh leading
cause of death, and 125,000 hospitalizations were recorded that
same year. In 2010, the WHO reported that 41 elderly individuals
die each day worldwide from external causes, representing this
group’s sixth leading cause of death, emphasizing that these events
remain incredibly underreported [13,14]. In our study, relatives,
mainly daughters, and sons, were the significant perpetrators
of abuse against older people, and the aggression occurred
inside the home. These data were similar to those observed in
medical literature. Alcoholism among the offenders was a factor
in many reports. Other similarities between the Brazilian results
and the descriptions in the literature showed that women were
the principal victims. The differences between the gender data
obtained in SINAN were mild, although women were the most
victimized every year [7,14]. These results were similar to those
of Souza et al. [15], perhaps reflecting the greater longevity of
women [15].
Although abuse against the elderly is not recent, only from
the late 1980s, it has become a topic of interest. Indeed, the
notifications recorded in the databases do not represent the actual
number of deaths or injuries suffered within this population. Under
notification might partially reflect that the clinical manifestations
of abuse are often mistaken for the manifestations of diseases
inherent to aging [15,16] (e.g, bruises). Some studies show that
the presence of bruises greater than five centimeters in size on the
lateral arm or right shoulder, neck, head, thoracolumbar region,
buttocks, and soles of the feet are not often observed in accidental
injuries and should be considered to result from an act of violence
[17].
Multiple wounds in different stages of cicatrization; bruises
at different stages of evolution (purple, brown, green, yellow, and
finally brownish yellow); injuries in the shape of any instrument,
marks of containment on the wrists, ankles, and heels; traumatic
alopecia, edema of the scalp, and fractured teeth or nose or
burns are other warning signs. Radiological images showed that
misaligned fractures in different stages of consolidation could
also be signs of physical aggression [17-21]. On the other hand,
pressure sores, localized areas of tissue damage, or necrosis are
common in the older population. However, Santos et al. [20] state
simple care can prevent pressure sores. Therefore, the presence
of these lesions might be an indicator of inadequate care. Is
important to point out that, as Mosqueda et al. [22] say, “innocent
bruises frequently occur on the extremities”. Although recognizing
this abuse is difficult due to physiological aging, it should always
be considered, particularly when the clinical history contradicts
the findings of physical or laboratory examinations.
Another explanation for the low rate of notification of abuse
against the elderly is that health professionals are not attentive to
this possibility and that older people frequently are silent victims.
The violence perpetrated through negligence principally occurs in
a domestic environment and frequently involves the family, which
makes diagnosis difficult. In many instances, the victim does not
report the violence because they do not understand the incident
as a form of violence or are afraid to report their close relatives
as the perpetrators. According to Minayo [13], deaths, injuries,
and trauma from traffic accidents and falls could also suggest
negligent acts committed by the authorities or people involved
with the accessibility of the elderly individual. Finally, we have
to remember that signs suggestive of sexual violence injuries
are also frequent, and include itching, anal or vaginal bleeding
and pain, sexually transmitted diseases, and spotting or bleeding
in the underwear21. Physicians, including coroners, should pay
attention to elderly individuals with these lesions during their
examinations, as there are many types of abuse against the elderly,
and the diagnosis is difficult.
The importance of our study is to show the large number of
cases of violence that occur in our country, totaling 23,542 cases
in the period studied, with an unspeakable tragedy represented by
the 1,111 deaths that occurred in this period. Notably, our study
also showed that assistance from health professionals facilitates
the identification and confirmation of cases of maltreatment
against the elderly and reporting, which is required by Brazilian
law. Worrisome, this study showed that the number of abuse
notifications against the elderly has grown in Brazil. Men and
women are victims of physical, sexual, and neglect abuse, often
recurrent and fatal. With the increasing life expectancy of the
world population, issues involving older individuals are becoming
common in medical practice. Among these issues, violence against
older adults must be analyzed carefully, as many forms of violence
leave no visible marks (neglect, psychological and economic
violence), and many injuries to the elderly result from advanced
age (fractures from falls and bruises). The information provided to
physicians does not always represent the truth. Thus, physicians
need to be alert to the possibility of violence against older adults,
as the recurrence of maltreatment, and to report suspected or
confirmed cases.
Journal of Forensic Sciences & Criminal Investigation
Background: Determination of sex using odontometric analysis in Dimasa Kachari population of Assam. Method: This study was conducted in rural area of Dima Hasao district in three villages (Lumbding, Maibang & Haflong) of Assam, India. 61 Samples were collected from were 29 female and 32 male subjects between the age group of 18-57 years. Dental casts obtained by alginate impression after informed consent. These samples were observed for the sexual dimorphism and to find out their individual accuracy level using canine and first molar teeth measurements of maxilla and mandible. Univariate equations were formulated for tooth measurements which can be used to predict sex and population when any of the single teeth is found at crime scenes or mass disaster cases and to set a standard for this population which can be used as a standard in forensic investigation, mass disaster, personal identity, sex estimation in future for the population of Dimasa Kachari of Assam, India. Conclusion: In This study mesiobuccal distolingual distance (MBDL) for first molar of Maxilla shows 67.2% accuracy and distobuccal mesiolingual distance (DBML) for first molar of mandible shows 73.8% accuracy for sex determination using Univariate discriminant function analysis.
Background: Sexual dimorphism using teeth has been extensively studied by means of odontometric analysis and most of the studies have shown statistically significant differences with t-test as well as with discriminant analysis based on data of permanent dentitions [1,2]. Odontometry has been performed on various tooth groups with the objective to establishing measurements that can act as standards and in this may also facilitate some procedures of dental surgeon as well as forensic odontology [3]. Tooth size standard based on odontometric analysis can be used as in age and sex determination [4]. In general, male teeth have been found to be larger than those of the female [5].
Assam has been the meeting place of different races and consequently a large number of ethnic groups have been residing in Assam. The Dimasa is one of the plain tribe of Assam inhabiting Barak Valley. They form a part of the greater Dimasa Kachari Society. Dimasa tribe, which belongs to Indo-Mongoloid Kachari group, is found in North East region of India. Tribal group Dimasa is one of the dominant tribal groups of the district of Dima Hasao. We have found no such studies on this tribe population regarding sexual dimorphism using dental measurements. The present study is an attempt to study the sexual dimorphism accuracy using canine and first molar teeth measurements of maxilla and mandible
According to studies, this has been reported that dental measurements have significant sexual dimorphism. Whereas it is also found that very few studies have set standards on population specific data which is of more importance. So, in the present study, an attempt has been given to set some dental data on the tribe population of Dimasa Kachari of Assam. This study would be helpful in finding out the relevant measurements which could differentiate sex with greater reliability as well as the individual accuracy level of all the measurements for sex determination by discriminant function analysis. These standards would be very useful help in identification of a person in any case of mass disaster, forensic analysis for personal identification, sex estimation etc.
Study sample was comprised of 61 dental casts obtained by alginate impression after informed consent, from 32 males and 29 females of the age group of 18-57 years, sharing same dietary habits (non-vegetarian). This study was conducted during March 2017. Samples were collected by door to door survey. Before sample collection, following general information was also recorded • Name of the subject • Age • Father’s sub-caste • Maternal uncle’s sub-caste Information regarding father’s sub-caste and maternal uncle’s sub caste helps us in evaluating the mating pattern among Dimasa Kachari population of Assam, India.
• Subjects having carious or missing mandibular and maxillary anterior teeth, • The presence of abrasion, attrition, spacing, and crowding, • Hard tissue diseases affecting the teeth were excluded from the study. • Sub-adults were excluded.
Dental impressions on mandibular and maxillary arches were taken in alginate and study casts were prepared using Dental stone by pouring it in alginate impression of both the mandible and maxilla [3].
I. Canine width (CW): It is taken as the greatest mesiodistal width between the contact points of the teeth on either side of the lower jaw. Measurements were taken with the caliper beaks placed [6,7]. II. Inter-canine width (ICW): It is measured as the linear distance between the tips of right and left canine [6,7]. III. Intermolar width (IMW): Distance between the lingual cusps of the corresponding molars in opposite quadrants. Measurements were taken on 1st molar [6,7]. IV. Buccolingual diameter (BLD):The distance between the outermost points of the molar crowns [6,7]. V. Meisodistal width (MDW): The greatest distance between the proximal surfaces of the molar tooth [6,7]. VI. Mesiobuccaldistolingual diameter (MBDL): The largest distance between the mesiobuccal corner and the distolingual corner of the crown [6,7]. VII. Distobuccal mesiolingual diameter (DBML): The largest distance between the distobuccal corner and the mesiolingual corner of the crown [2].
To accomplish the objectives of the present study, the data have been analyzed statistically. The chapter deals with the presentation and interpretation of the following aspects: • Assessment of sex differences in all the tooth measurements of Dimasa Kachari population of Assam, India. • Formulation of univariate discriminant function equations of all the tooth measurements • Assessment of original and cross-validated accuracies of all univariate discriminant functions.
Data were subjected to descriptive statistics and t-test for assessing sex differences. Table 1 represents the mean, standard deviation, standard error of mean and dispersion for all the measurements of male and female tooth of maxilla. It is observed from the table that all the measurements have greater mean values for males except ICW and MDW which indicates that male teeth of maxilla are greater than female. Further it is noticed that the range values are higher in the ICW than other measurements in both male and female.
Table 2 represents the mean, standard deviation, standard error of mean and dispersion for all the measurements of male and female tooth of mandible. It is observed from the table that except than ICW, all the tooth measurements of mandible have greater mean values for males which indicate that male teeth are larger in size. Further it is noticed that the range values are higher in the ICW of female than other measurements which is also greater than the range value of male.
Table 3 Represents the results of t-test in order to access the sex differences in different tooth measurements of maxilla. It is observed from the table that all the measurements show significant sex difference (p> 0.05 level) except than ICW IMW and MDW. Table 4 presents the results of t-test in order to access the significant sex differences in different tooth measurements of maxilla. It is observed from the table that all the measurements show significant sex difference (p> 0.05 level) except than CW and IMW.
Table 5 Represents the raw coefficients, centroids, sectioning point, original accuracy percentages and cross-validated accuracy percentages of all the different measurements of male and female of maxilla. It is observed from the table that the highest significant contribution and accuracy is achieved from MBDL (original accuracy=67.2%; cross-validated accuracy=67.2%) and the least is achieved from MDW (original accuracy=52.5%; cross-validated accuracy=52.5%).
Table 4 presents the raw coefficients, centroids, sectioning point, original accuracy percentages and cross-validated accuracy percentages of all the different measurements of male and female of mandible. It is observed from the table that the highest significant contribution and accuracy is achieved from DBML (original accuracy=73.8%; cross-validated accuracy=73.8%) and the least is achieved from CW (original accuracy=53.5%; cross-validated accuracy=53.5%).
Original and cross-validated accuracies of all the univariate discriminant functions are calculated by discriminant function analysis and are presented in bar Graph 1. It is observed from the graph that in maxilla the highest original accuracy and cross-validated accuracy is achieved from MBDL (original accuracy=67.2%; cross-validated accuracy=67.2%) and the least is achieved from MDW (original accuracy=52.5%; cross-validated accuracy=52.5%) (Grapgh 2).
It is observed from the graph that in mandible the highest original accuracy and cross-validated accuracy is achieved from DBML (original accuracy=73.8%; cross-validated accuracy=73.8%) and the least is achieved from CW (original accuracy=53.5%; cross-validated accuracy=53.5%). Male values were exceeded female in all the observed dimensions except intercanine width, which shows reverse dimorphism.
The major work carried out by Garn et al. [2] identified the mandibular canine as displaying the greatest sexual dimorphism in human dentitions, and using a combination of teeth, reported an 86% success rate in correctly identifying sex. In our study mandibular canine does not show significant t-value but other molar measurements are giving the accuracy up to the level of 73.8%.
Further, in maxilla, mesiobuccal distolingual distance has got the highest accuracy that is 67.2% and in mandible distobuccal mesiolingual distance shows the highest accuracy that is 73.8%. Thus, these univariate equations are of very much practical importance and can be used for predicting sex with greater reliability.
Male values were exceeded female in all the observed dimensions except intercanine width, which shows reverse dimorphism. Reverse sexual dimorphism has also been observed in other populations Ticuna Indians Colombia, Nepalese, South Asian (Indians) [8].
According to recent studies, molars are more reliable for sex estimation as it shows highly significant sexual dimorphism. While comparing the results of recent studies with the present study it was found that male’s teeth are larger than females which are in the agreement of present study. In present study we included canine and 1st molar measurements for finding significant sexual dimorphism [9,10].
In our study we have found that in maxilla, male’s shows higher mean values than females except inter canine width that is higher in females. The major work carried out by Garn et al. [2] identified the mandibular canine as displaying the greatest sexual dimorphism in human dentitions, and using a combination of teeth, reported an 86% success rate in correctly identifying sex. In our study mandibular canine does not show significant t-value but other molar measurements are giving the accuracy up to the level of 73.8%. Further, in maxilla, mesiobuccal distolingual distance for first molar has got the highest accuracy that is 67.2% and in mandible distobuccal mesiolingual distance for first molar shows the highest
accuracy that is 73.8%. Thus, these univariate equations are of very much practical importance and can be used for predicting sex with greater reliability.
Values for male’s were exceeded than female in all the observed dimensions except intercanine width, which shows reverse dimorphism. Reverse sexual dimorphism has also been observed in other populations Ticuna Indians Colombia, Nepalese, South Asian (Indians). (Acharya et al., 2007).
Data have been analyzed statistically by SPSS 16 for mean, standard deviation, standard error, dispersion, t-test, and univariate discriminant function analysis. By using all these statistical analysis in different measurements of tooth following observations were found in maxilla and mandible:
• Highest percentage for correct sex classification was found for mesiobuccal distolingual distance by univariate discriminant function analysis that is 67.2%. • Least percentage for correct sex classification was found for mesiodistal width by univariate discriminant function analysis that is 52.5%.
• Highest percentage for correct sex classification was found for distobuccal mesiolingual distance by univariate discriminant function analysis that is 73.8%. • Least percentage for correct sex classification was found for canine width by univariate discriminant function analysis that is 52.5%.
i. Ethics approval and consent to participate: The study was carried out after obtaining written consent from the subjects. The whole maneuver had been explained to the subjects and any unexpected risks that may appear during the course of the research had been declared to participants. ii. Availability of data and materials: “The dataset supporting the conclusions of this article is included within the article is included within the article and its additional file”. iii. Competing interests: The authors declare that they have no competing interests. iv. Funding: This study was funded by the authors. v. Author’s contributions: Kirti Sharma collected the samples, prepared impressions, analyse the data. Dr. Rajeev Kumar corresponding author revised this study. Dr. Deepali Jain analyzes the data, chooses the methodology and wrote the drafts of this study. All the authors read and approved the final manuscript
Iruña-Veleia is an archaeological site located in northern Spain, 10 km away from the city of Vitoria- Gasteiz (Figure 1). In the Bronze Age, 800 years B.C., there were already indigenous people. From 40-30 B.C. the Roman Empire took control of Iruña-Veleia and around the first century A.C., they started to build a Roman-style city. From the first to the third century, the city was at its peak. There were between 6000 and 8000 inhabitants and the city was 80 hectares in area. In the third century, the Roman crisis led to a decrease in size; there was a decrease from 80 hectares to 10. In addition, city walls were built around it (Figure 1). In the sixth century A.C., the city was abandoned. In the following centuries (around 1300 years), there were forests and crop fields. These days, 5 per cent of the archaeological site was excavated. In 2005 and 2006, about 400 objects containing graphite sketches were found (called ostraca) during the archaeological excavation led by Eliseo Gil, who had been working at the archaeological site since 1995. Archaeologists found more than 400 archaeological objects which had writing or drawing scratched into them. The technique used to write these texts consisted of doing freehand line grooves on different materials by using tough objects, without using any guide or stencil. The texts found had been written on pottery, clay, Hispanic terra sigillata (HTS), bone, brick and mortar.
Texts were written in Latin, Basque and Greek. They covered a wide range of themes: alphabets, anthroponomy, classical names, the Creed, Christian characters, Egyptian names, sentences in Basque, lists of Basque words, myths, Roman religion and texts in Latin. The ostraca analyzed in this study were found in six different sectors and within a total of nine archaeological strata. All that diversity made the team of archaeologists think of the existence of a school. The chronology assigned to those objects dated from AD 100 to 500. After publishing the photos of some of the ostraca found, experts in various fields casted doubts on the authenticity of the texts up to the point of accusing the leader of the excavation of falsifying those texts and further stated that he had written them himself after unearthing the archaeological objects with the purpose of gaining professional reputation. As a result of those accusations, the excavation team was dismissed. Nowadays, there is a new team of archaeologists working in that archaeological site.
The starting point of this study was the concern about the authenticity or falseness of the texts scratched into the ostraca found at the archaeological site of Iruña-Veleia [1]. in northern Spain from 2005 to 2008 by the team of archaeologists hired. Controversy has broken out over whether those texts were written in the Roman period (if it be so, texts would be authentic) or whether the texts were written after being unearthed by the archaeologists from 2005 to 2007 (being that the case, texts would be false). The materials on which texts were scratched date back to the Roman period, but doubts have arisen over the date those texts were written: either in the first centuries A.C. or in the twenty first century. All the ostraca are in question and opponents of the authenticity of texts claim that all of them are fake; supporters, however, state that every text is authentic. After 10 years of discoveries, thus far there is only one scientific book that defends the authenticity of those texts [2]. There is not any scientific magazine article reviewed by experts that deals with this issue. Pieces have been taken to a laboratory three times in order to carry out a physical-chemical analysis.
The first analysis was rejected, for it did not comply with minimum required requirements, the second analysis was aborted before finishing it and the third analysis was not published even if it had been financed with public funds. The owner of the ostraca and the archaeological site, the local government, does not allow any study on the ostraca nor any exploration of verification at the archaeological site aimed at finding new ostraca. It is surprising that bones have not been analyzed yet; it is easy to scratch fresh bones, but it is difficult to do so on old bones. Dating bones with texts written on them could end this controversy. It is also surprising that dating analyses of bricks with texts written on them have not been carried out before baking the bricks in an oven. In short, materials have not been at the disposal of the scientific community for 10 years. In those 10 years, the only possibility of making scientific progress was studying the photos of the ostraca. In order to understand this difficult situation, it should be noted that texts written on the ostraca have seriously questioned highly established scientific theories on the reconstruction of the Basque language in the Roman period, on the migration of population in the fifth and eighth centuries and on the introduction of Christianity in the Basque region. Here is a small summary, without going in depth, of the arguments for and against the authenticity of texts.
a. Some ostraca have letters covered by mineral deposits.
b. On top of the marks of the letters of the texts, there are are crystallizations of calcium carbonate.
c. Incisions on bone are only possible on fresh bone, not on very old bones.
d. There are ostraca scratched on brick before the baking.
e. Team of archaeologists that has great credit worthiness and 10 years of experience.
f. High quality archaeological practice according to Edward Harries, creator of the Harris Matrix method [3].
g. The set of ostraca is coherent with the Roman period.
h. There are parallelisms of drawings and shapes of letters of that period.
i. The shapes of the allographs are from the Roman period.
In general, supporters of the authenticity ask for a laboratory analysis and trial excavations at the archaeological site with the participation of the international scientific community.
a. Texts contradict the theoretical reconstruction of the Basque language of the Roman period.
There is not any prior text to the discovery of the ostraca in the eleventh century regarding the Basque language.
b. Texts contradict the established theory about the nonexistence of Basque-speaking population in Iruña-Veleia during the Roman period.
c. Texts contradict the established theory about the introduction of Christianity in the region after the eighth and ninth centuries.
d. Impossibility of NIIPIIRTITI, NIIPIIRTARI y NIIFIIRTITI in the Roman period. It should be read Nepertiti, Nepertari y Nefertiti.
e. Impossibility of reading RIP on a crucifix.
f. Impossibility of very evolved forms from Latin: PLUTON, FEBO, BACO…
g. Impossibility of ANQUISIIS, since in that period only ANCHISIIS was possible.
h. Impossibility of CVORII, which is a very evolved form of Latin.
i. The shape of the M allographs is not from that period.
In general, supporters of the falseness of texts deny the need for a laboratory analysis and trial excavations at the archaeological site, which are claimed to be unnecessary, since the falseness of text has already been proven.
Here is the ultimate question to which this study aims to respond: are those texts current falsifications or were they written in the Roman period? The answer to that question is multidisciplinary and beyond reach, but from the forensic science of text analyses, an attempt has to be made to contribute to responding to the general question. And regarding the discipline of forensic analysis of handwritten texts, the questions to respond to are: is there a single author or, on the contrary, do the texts written on the ostraca have several authors? Is the size of the ostraca related to the size of the letters? Are there different groups of writing regarding the various materials, themes, strata or languages? If there are different groups of letters regarding different factors, what scenarios are the most probable to explain it? Figure 2 shows the flow chart of the study.
In the event that there is a single writer, it is expectable for the measurements taken to be homogeneous results. On the contrary, if there were several writers, the results of the study would be heterogeneous. In the first part of this study, an attempt will be made to determine whether the distances measured are heterogeneous or homogeneous. When facing the dilemma about deciding whether the null hypothesis (H0) was the heterogeneity or homogeneity of the distances, it was observed that the objects with texts written on them had been found in five different sectors, in nine strata from different epochs. Moreover, they dealt with 11 themes, they had been written on nine different types of material and in three different languages. Therefore, as a starting null hypothesis, there may be diversity in the measured distances as well. Consequently, the null hypothesis was suggested as follows: [HA0] The characters of the texts are heterogeneous regarding the three distances measured the alternative hypothesis would be the following:
[HA1]The characters of the texts are homogeneous for the three distances measured The statistical task was to try to define the homogeneity or lack of homogeneity of the features extracted from the sample, in order to establish the degree of similarity between the letters from different ostraca. If the writing were homogeneous regarding the different ostraca (HA1), on the one hand, the different distances should show a normal distribution, and they should present dependence of data. And, on the other hand, the associations between the distances and the stratum in which they had been found, the theme and the language of the ostraca should not exist.
In the second part of this study, in case that the [HA0] could not be rejected, that is to say, if the results pointed to heterogeneity, the source of such heterogeneity would need to be identified. The hypothesis to be tested may be formulated as follows. [HB0] Height and width of ostraca explain the observed variability. [HB1] The size of the ostraca not explain the observed variability. Height and width are dismissed as explanatory variables of the heterogeneity and, therefore, the type of material, the writing instrument and the intent to deceive are left. The aim of this part is to observe whether the ostraca containing big letters are correlated with ostraca which are big in size. And, on the contrary, whether ostraca small in size contain small letters.
This third part of the study aims at identifying the source of such heterogeneity, in the event that distances were heterogeneous and [HB0] was rejected. In any group of texts, the source of the variability observed may be due to different hand writers, different surfaces or different writing instruments. Furthermore, in the event that there was only one hand writer, their intent to deceive could modify the variability observed. From the variables pointed out, this study only takes control of the physical surface. Thus, here is the third hypothesis to be tested: [HC0] The physical surface variable explains the observed variability. [HC1] The physical surface variable not explain the observed variability. In other words, the variability observed is due to different hand writers, the writing instrument or the intention of modifying the writing of a single writer. Not rejecting the hypothesis of the variability explained due to the physical surface [HC0] leads us to the existence of a single writer, without getting to prove it (Figure 2). And consequently, it provides evidence of the falseness of the texts of the ostraca. Rejecting the hypothesis of the variability explained due to the physical surface [HC1], without getting to prove it, provides evidence of the existence of several hand writers, the existence of several writing instruments or the existence of a single writer with intent to deceive. The existence of several writers provides evidence of the authenticity of the texts written, without considering it a proof, of course.
In this last part, an attempt will be made to describe the variability observed in the following factors: ostraca factor, stratum where they were found factor, theme factor and language factor. Those are factors which cannot be the cause of the variability of the distances measured. An attempt will be made to describe whether the different modalities of the factors present homogeneous or heterogeneous distances. For instance, if texts show heterogeneous distances in different strata, it will not be correct to confirm that it is the stratum what directly affects the distances. Quite the opposite, there is another variable that has not been observed, which makes it possible for the distances of the letters of the texts on different strata to be different.
An attempt will be made to create coherent scenarios that interpret the results obtained. Theoretical framework The studied allographs are: (23 allographs): “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”, “I”, “J”, “L”, “M”, “N”, “O”, “P”, “Q”, “R”, “S”, “T”, “V”, “X”, “Y” and “Z”. Regarding the ostraca on scale, there are not more allographs, those are the only ones. There is not any lower case character. In this article, the word character is any symbol trying to represent an allograph; any of the above listed, whether it is repeated or not. The following example helps distinguishing an allograph from a character: in the text “NIIV MI TA RIIBA II LABA”, there are 19 characters and 9 allographs (N, I, V, M, T, A, R, B and L). There may be writing on both sides of the ostraca. In this article, ostraca refer to shreds which have writing on them. That is to say, an archaeological object with writing scratched into both sides will be considered as two independent ostraca. According to several authors [4-7], the most important allograph features, to be analyzed by the researcher are as follows: arrangement; class of allograph; connections; design of allographs (alphabets) and their construction; dimensions (vertical and horizontal); slant or slope; spacings, intraword and interword; abbreviations; baseline alignment; initial and terminal strokes; punctuation (presence, style, and location); embellishments; legibility or writing quality; line continuity; line quality; pen control; writing movement (arched, angular, interminable); natural variations or consistency; persistency; lateral expansion; and word proportions.
It is important to point out that the studies carried out by those authors are neither on texts written on ostraca nor on writings obtained through eliminating apart from the surface of the writing. In the study that analyzes two texts written by each of the 21 writers analyzed, Lizeaga [8] establishes that the relative height and width are individual and distinguishing features of at least 19 writers, even 21, depending on the statistical technique applied. This study is far from being considered a writer identification study. Among many studies on the analysis of manuscripts in the case of the ostraca found at Iruña-Veleia, an attempt was made to extract the micro-features described by Srihari [4] and, in addition, the letters written on the ostraca were characterized by the micro-features described by that author. The majority of the micro-features were non-existent in those ostraca, and the variability of the micro-features was extreme. Among those micro-features that were actually found, there were not any homogeneous characteristics with regard to the same ostracon. Therefore, the procedure suggested by Srihari was unsuccessful when analyzing those ostraca. Among the causes for failure in the application of the techniques suggested by Srihari, the fact that letters were written in upper case should be taken into account, along with the writing techniques used, the private character of the texts, which gave the author greater freedom regarding the geometry of allographs.
With regard to historical writings on hard writing surface by grooving, the following studies should be pointed out. On archaeological material and, specifically, on Athenian inscription, Tracy [9-12] established that, currently, it is possible to identify ancient letter-cutter individuals. The author studied hand writers in Greek decrees of 2nd and 1st centuries B.C. Those were text written by chisel and with great skill by a mason. By applying criteria such as careful appraisal, profile of letter and uniformity, the author assigned several inscriptions to one specific author. That technique assumed that stonemasons reveal individual characteristics. Tracy observed that the texts he analyzed were written freehand with the help of some guidelines. In more recent studies, other authors [13-15] suggest techniques for segmenting the images of characters, removing the outline and comparing through various statistical techniques in order to identify writers, regarding texts written on hard surfaces by eliminating substrate.
An attempt was made to use those methods, but there were negative results. The 77 inscriptions haven’t been photographed according to a strict protocol and the owner of the ostraca does not allow to take new photos. Different resolutions, focusing, brightness and contrasts require to use different algorithms to segment the letters of the different ostraca, with non-controlled effects for statistical treatment. Moreover, the texts of the ostraca are private, they were not written by artists, but by people/ person who lacked expertise and had little experience, for the writer did not care about the aesthetics of letters. Those texts are not aimed at being read. What is more, from the statistical point of view, for instance, there is a maximum of 12 characters for the allograph “A” and five characters of the allograph “A” on average. Method of measuring Taking into account that there were not many letters of each allograph along with the specific features of the ostraca, an attempt was made to look for measurable common features for all the allographs and all the texts of each ostracon. These are the three distances measured:
d1) The height of letters. It is the height of the imaginary rectangle that forms the letter.
d2) The distance between letters in the word, or the distance to the following letter. It is the distance between the imaginary rectangles that form two contiguous letters in the same line and word.
d3) The distance between lines of writing, or the distance to the letter above. It is the distance between the imaginary rectangle of a letter in a line and the rectangle of the letter which is right below it in the following vertical line.
At the beginning of the study, in a coordinate system, distances were calculated so that the maximum of a coordinate was deducted from the minimum. It was observed early that it complicated the aims of the study. That is why the rectangle that generates each letter was studied and interpreted. An attempt was made to always interpret the intention of the writer. The measurement of the rectangles that generate letters is subjected to bias. In those type of materials, writing texts by punch, which tears material from the substrate, led to chip the material deeply, what lengthened, narrowed or masked the stroke. A very strict definition, based on pixels, of the distances would lead to biases in the study due to the punch and the undesirable substrate. That is to say, a measurement of the letters by an automatic system may distort the intention of the writer. Let us not forget that those are writings by people who had little expertise and texts were private. At least the biases created owing to the human measurement are smaller than those owing to the nature of the material
It is important that there was not any change of measurement criteria when the measuring the distances. Criteria were respected throughout the study, for there was only one person who measured all the distances. After all, this study aims at comparing whether there are any groups of very high or very low letters. That is, whether there are groups of very wide or very narrow spaces. And whether there are groups of very wide or very narrow line spacing. Or, on the contrary, whether there is one single group of height of letters, one single group of spacing between letters and one single group of distance between letters. From that point of view, the measurement error introduced by a meter would be equally distributed in the aforementioned groups if there were any. In other fields, such as dendrochronology, in which the growth rings of trees are measured with the purpose of dating historical objects, the width of rings is measured according to the researcher’s criteria and very strict automatic measurements are of no use. Regarding these photos, which have different features, very strict measurements of the distances led to the creation of many undesirable issues and artifacts. This work was based on the photographies of the ostraca (Figure 3).
After selecting the handwriting sample to study, the three distances were measured separately. The three distances of letters were measured using the application On Screen Measurement [16]. This is a software specialized on measuring growth rings of a tree on high-resolution scanned images. The application OSM makes it possible to create tiedown points with the purpose of measuring on straight lines. Besides, it creates reference lines to keep the required perpendicularity. The following link https:// www.youtube.com/watch?v=Mp4egetsPkE shows a video on the procedure for measuring the three distances. An attempt was made to imagine how the writer would have written that character. In order to avoid possible distortions of the study, allographs which overlapped, were not identifiable or lacked proper writing were not measured. There is another free application for similar purposes called ImageJ [17].
Nowadays, those ostraca are held by the Deputation of Araba (regional government) and it is not permitted to study that archaeological material. Thus, we were obliged to carry out this study based on scale photographs of the archaeological objects. Those ostraca containing text which were in a well-known scale were selected. 65 ostraca were studied, there being a total of 77 sides containing writing on them, and 1608 heights, 1051 distances between letters and 836 distances between lines in the text were measured. Table 1 shows the amount of letters of each ostracon. The file of measures of all the ostraca are in Zenodo repository [18]. The file of photos of all the ostraca are in Zenodo repository [19]. After selecting the sample to be studied, the height of letters (d1), the distance between the adjacent letters (d2) and the distance between letters in different adjacent lines (d3) were measured. Fig. 3 represents every measurement on an ostracon. In the case of the distance between adjacent letters of the same word, the measurement was conditioned by the character. For instance, the majority of “T” letters were on top of the previous or the following letters.
When the distance between letters was non-existent or doubtful, it was neither measured nor included in this study. With regard to curved allographs or gradient distances, an attempt was made to put oneself in the writer’s place and imagine the space that person would assign. Regarding the distance between letters from different adjacent lines, the procedure used for measuring was the same as the one used for measuring the distance between adjacent letters. The resolution of the images was a non-controlled variable. The rank of the distance d1 varies between 0.17 cm and 3.1 cm. The attributes of the writing surface, language, stratum and theme were given by the archaeologist Idoia Filloy, who was deputy director of the excavation of the archaeological site, and they were consulted on the Internet in the Ostraca base database [1]. The writing surfaces of the ostraca may be: Clay (one ostracon), Amphora (one), Storage Pottery (one), Common Pottery (23), Bone (seven), Brick (13), Common Mortar (three), Cooking pot (one) and Hispanic Terra Sigillata (27).
There are ostraca containing writing in Latin (44), in Basque (30) and in both languages (three). And the range of themes may be the following one: Anthroponym (five), Classic (eight), Creed (three), Christian (17), Egyptian (four), Sentence in Basque (19), Lists in Basque (three), Myths (five), Roman Religion (four), Text in Latin (three) and Non-classified (six). They were found: in sector 12 (stratum 12007) 1 ostracon; in sector 3 (stratum 3001B) one ostracon; in sector 32 15 ostraca (in the stratum 32005A, six ostraca; in the stratum 32005C, nine ostraca); in sector 5 (stratum 51144) 32 ostraca; in sector 6 (in the stratum 6076 21 ostraca, in the stratum 6180 five ostraca, in the stratum 6181 one ostracon) and in sector Finca 95 one ostracon. Statistical analyses Taking into account the different nature of the data, here are the statistical techniques used:
That is to say, the existence of spatial independence, linear autocorrelation, strictly speaking, between the height of adjacent letters. Sometimes, the knowledge of the height of a letter provides one with the information about the height of the following letter. This phenomenon is known as spatial autocorrelation [20]. Among the possible causes for that autocorrelation, one may think that the writer would look at the last letter or the previous letters written to draw the next one. Two types of autocorrelation may be found, a negative autocorrelation in which the height of letters alternates, for the writer corrects the heights. And a positive autocorrelation in which high letters precede high letters and reversed. In that case, interpreting it is more difficult. Therefore, first-, secondand third-order autocorrelation coefficients were calculated by using the “acf” function of the application R [21]. 0.95 confidence bands regarding the value of the coefficient of autocorrelation were estimated in a distance of 2/√N where N was the number of letters on the ostracon. Regarding the ostraca, only the firstorder coefficient was significant.
Correlation Between the Distances and The Size of The Ostraca
Regarding those ostraca without autocorrelation that have a normal distribution, the R² coefficient of determination was calculated regarding the linear model. The response variables were d1, d2 y d3, whereas the width and the height of the ostraca were explanatory variables.
Statistical Normality
The next question to answer was whether the set of all the letters from the ostraca showed a normal distribution regarding different distances. Due to the need for independence of data, ostraca containing autocorrelated letters were not included. The boxplots of the mean of the distances measured in each ostracon were extremely useful tool. Another technique regarding the determination of statistical normality is the goodness-of-fit test called the Shapiro-Wilk test [22]. It is a test in which it is not necessary to specify the parameters of the normal distribution in the null hypothesis of normality. The non-normality of the three distances in the majority of modalities of writing surface, stratum and theme showed that it should be observed whether part of this variability was associated to the levels of the different factors to know: writing surface, language, stratum, theme and ostracon. Thus, the appropriate statistical technique to apply was the analysis of variance.
Analysis of Variance
According to Ugarte [22] the distances would be the variable response, letters would be the experimental units and the ostracon, the writing surface, the language, the stratum and the theme would be the factors. The hypothesis were contrasted, in which the model was as follows:
H 0 :1=...=k in the null hypothesis, it was established that the measurements of the distances of the levels of the factors were equal and the alternative hypothesis was that at least the mean of a level was different to the rest H 1 : ∃μi≠μ j for some i≠ j . The model regarding the null hypothesis was as follows:
yij=μ+ εij In which yij the distance j of the level i . μ was the average of all the differences measured and εij was the error due to randomness of the distance of the level i .The model of the alternative hypothesis was as follows:
yij=μ +α j +ε ij
In this case, α j was the effect of the modality j . As the model did not admit autocorrelated data, 11 ostraca were removed regarding d1 distance, three regarding d2 distance and 10 regarding d3 distance.
Another aspect that was taken into account was the heterocedasticity of the measurements, that is to say, when the expected value of the variance depends on the value of the measurement of the sample. Ostraca containing letters of great mean distances had high variances and in reverse. In order to carry out an analysis of variances, the variances needed to be homogenized. Thus, logarithms of the distances were calculated (20) by which homoscedastic data were obtained.
Analysis of relative height of the different allographs
That is to say, observing whether the relative height of some allographs with regard to others (A, B, C...) was similar in different ostraca [8]. It was questioned whether the information about the height of different allographs could be used. Each handwrite might have had a characteristic distribution of the height of the different allographs. For example, one writer might have written the letter “S” especially high, and the letter “Q” especially low. In that case, the two ostraca analyzed had a similar profile. With the purpose of obtaining a sample of the height of the homogeneous allographs, regarding the mean and the variance, the height of all the letters from all the ostraca was standardized by subtracting the mean of each ostracon and dividing it by the standard deviation (to obtain a normal distribution N(0,1)) of each ostracon. That mean value of each allograph was called the mean ostracon character. Those allographs whose relative height was higher than that of the mean of the allographs from the texts would have a standardized positive value, and those allographs whose mean height was lower than that of all the allographs from the ostraca would have negative indexes.
Afterwards, all the allographs from the ostraca were gathered. That series was called Mean Allograph Vector (MAV) of the ostracon. For instance, if the letter “B” appears five times on an ostracon, the mean of the standardized values of those five “B”s had to be calculated. Subsequently, the set of the mean values of the allographs “A”, “B”, “C”,... was called MAV of that allograph. Then, the MAVs from those ostraca containing more than 10 different allographs were compared. By calculating the correlation between the MAVs from different ostraca, an attempt was made to search for similarities.
From a total of 77 objects containing writing, 11 showed autocorrelations with a significance level of 0.95 regarding the height of letters (d1): 11139, 11429, 11530A, 11530B, 13364, 11267, 11425, 13380, 13382B, 15923, 16362B. On the contrary, the remaining ostraca (66), in which there were 15 measurements at least, did not show autocorrelation. That is to say, the following letter did not provide with any information concerning the height of the letter measured. Regarding the distance between letters (d2), just three out of 73 ostraca with sufficient measurements of distance between letters showed autocorrelation: 11423, 13397B, 15912. Principally, they did not show spatial autocorrelation. Regarding the distances between different lines (d3) in the text on 10 ostraca, from a total of 54, significant 0.95 autocorrelation was observed: 11139, 11429, 11530A, 11530B, 13364, 11427, 13368A, 15912, 15921, 163653. On the contrary, the remaining 44 ostraca did not show any autocorrelation. Therefore, the dependence between the data was different with regard to the different ostraca, and, moreover, any association of the autocorrelation could not be established by looking at features such as language, theme, stratum or writing surface. There are five ostraca which show autocorrelation regarding d1 and d3. It is thought that ostraca containing a greater number of letters reveal tendency towards autocorrelation.
Regarding autocorrelation, a diverse reality may be observed.
The coefficients of multiple determination (R²) were: d1~width+height 0.30; d1~ width+height 0.26 and d3~width+height 0.25. The coefficient of determination measures the fraction of the variance of response variable which is explained by linear model. It is observed that the height and width explain more or less 30 per cent of the variability observed. It should be noted that the measuring width and height of the ostraca was not simple owing to the irregularity of the dimensions of the material. In case of doubt, the space available for writing was estimated as the value of that dimension.
There were several ways of analyzing normality: the first one regarding all the letters measured in all the units was to use the Shapiro-Wilk test of normality. Regarding d1 distance of the set of all the letters from all the ostraca, the result of the test was a p-value equaling 0. As a consequence, the normality hypothesis with regard to the height of letters was rejected regarding the set of letters from all the ostraca together. Regarding the distances d2 and d3, the results of the Shapiro-Wilk test of normality were similar to those of the distance d1. The set of letters regarding the three distances studied did not show a normal distribution. Normality may be analyzed differently regarding the letters of each individual ostracon . Figures 4-6, shows the great heterogeneity of the distances of the ostraca via bloxplot representation. In the boxplot, each box represents a unity of ostracon, it may be easily observed that boxes were grouped together regarding zones in the graphic which corresponded to archaeological sectors and stratigraphic strata. Furthermore, the Shapiro-Wilk test was used to measure the normality of distances of each ostracon separately. The sample would be formed by those ostraca which did not show autocorrelation and those which had more than 10 letters. stratum and theme, and the three distances studied. Modalities of more than 3 ostraca. In brackets, the number of ostraca included in the test.
Applying the Shapiro-Wilk normality test with regard to the d1 distance of each individual ostracon resulted in high p-values, and the normality hypothesis could not be rejected regarding each ostracon. There were only three exceptions 13380, 15921 and the ostracon 21658, in which the normality hypothesis could be rejected. The distribution of d2 distance between letters of each ostracon individually was normal. And regarding d3 distance, there were only 2 ostraca that did not present a normal distribution in the distance between lines, ostraca 15921 and 11530A specifically. It was interesting to know whether the different distances showed normality or not in different materials, in the different strata found, and regarding the different themes these ostraca dealt with. As individual ostraca showed normality, just those modalities of more than 3 ostraca were taken into account. Results of the Shapiro-Wilk test were summarized on Table 2. In the hypothesis HC it is contemplated whether the materials containing writings were the cause of the heterogeneity. Boxplots (Figures 7-9). may be helpful to clarify it. In those boxplots, homogeneity was not observed regarding various materials containing writings.
Results were summarized on Table 3. Regarding the height of letters (d1), when analyzing which one of the factors explained greater amount of variance, the own ostraca stood out with 90 per cent of the total variance. Furthermore, it was found that the theme and the type of writing surface explained approximately 50 per cent of the total variance, respectively. Regarding the type of writing surface, the explained variance (R²) was calculated with regard to modalities, removing each modality successively. Results showed that without the ‘brick’ modality, the coefficient of determination or the explained variance was 22%. The ‘brick’ modality was rather homogeneous, and therefore, special, for it was the one that contributed the most to the rejection of the hypothesis of equal means. With regard to the factor theme, the explained variance was calculated, removing each theme successively, but there was not any level that contributed to the explanation regarding variance.
That was due to the high variability found in all the themes. Finally, the different strata explained the low variance observed (34 per cent) and the languages of writing explained an even lower variance (4 per cent). With regard to the distance between letters (d2), when analyzing which one of the factors explained greater amount of variance, the own ostraca stood out with 74 per cent of the total variance. In addition, it was found that the type of writing surface explained 53 per cent of the variance. Regarding the type of writing surface, the explained variance (R²) was calculated, removing each modality successively. Results showed that without including the ‘brick’ modality, the analysis only explained 26 per cent of the total variance. Therefore, the ‘brick’ modality was rather homogeneous, and consequently, special. Both the thematic differences of the ostraca and the different strata of the ostraca found as well as the language were intrinsically heterogeneous, and it was not possible to distinguish any modality, and it explained low variability.
With regard to the distance between line (d3), when analyzing which one of the factors explained greater amount of variance, the own ostraca stood out with 64 per cent of the total variance. Moreover, it was found that each the type of writing surface and the theme explained approximately 30 per cent of the variance. Both the different writing surfaces and the different themes of the ostraca as well as the different strata of the ostraca found, and the language were intrinsically heterogeneous, and it was not possible to distinguish any modality. Regarding the three distances, the ostracon factor was clearly the one that explained most variability, 91per cent. The type of writing surface, stratum and theme were far behind. Attention should be drawn to the great number of data of each modality, for in some cases, there were modalities that could have 442 data. Thus, the ANOVA contrast tended to reject the null hypothesis of equal means.
With regard to relative height, a diverse and heterogeneous reality was again to be observed between the different MAV of each ostracon. Any pattern could not be found. Many correlations were negative. On the other hand, there were few correlations between positive MAVs, and any coherent set was obtained.
Summing up the set of results, it may be observed that the analyses of the autocorrelation of the 3 distances showed that there were some ostraca that showed autocorrelation and some others that did not. In addition, the autocorrelated ones could have positive or negative correlation. On the other hand, the analysis of normality established that each ostracon mainly had a normal distribution with regard to the three distances measured, but the whole did not have a normal distribution. The ‘brick’ type of writing surface regarding d1 and d3 showed normality in distances. The stratum “32005A” regarding d1, d2 and d3, the stratum ‘32005C’ regarding the three distances, the stratum ‘6076’ regarding d3, and finally, the stratum ‘6180’ regarding d2 and d3 showed normality. With regard to the theme ‘Anthroponym’ regarding d1 and d2, and the theme ‘Christian’ regarding d3 showed normality. The ANOVA showed that the greater variability was the one explained by the individual ostraca, with regard to the writing surfaces, the ‘brick’ modality was the one explaining greater variability and the rest of modalities were not very important with regard to heterogeneity. Regarding language, the variability of each language was similar. The variability explained by the strata approached 33 per cent. Finally, the different themes did not explain much of the variability observed, between 30 per cent and 50 per cent. The analysis of the relative height of different allographs showed a great diversity once again, without the existence of any pattern.
According to the results obtained and with regard to the null hypothesis of the study:
[HA0] The characters of the texts are heterogeneous regarding the three distances measured
It can be claimed that the results of the four analyses shown did not give signs to reject the null hypothesis of heterogeneous texts with regard to the three distances. Texts were heterogeneous regarding the three distances.
The analyses of correlation between the distances measured and the size of the ostraca explain 25-30 per cent of the variability observed. Therefore, there is still 70 per cent is to be explained. We may partly affirm that there are enough signs to reject hypotheses HB0.
According to the results obtained and with regard to the null hypothesis of the study: [HC0] The variability observed is explainable by the physical surface variable. The boxplot (Figure 7) ostracon/surface regarding d1 shows that the ‘brick’ modality, in general, presents letters which are higher than those in other modalities. Moreover, it is rather homogeneous. It is interesting to point out that the brick ostraca were all found in ‘sector 3’. With the exception of one, the ‘bone’ modality is homogeneous regarding average height of similar letters. ‘Pottery’ and ‘HTS’ modalities are heterogeneous and have wide variances. The boxplot (Figure 8) ostracon/surface regarding d2 shows that in the ‘brick’ modality distances are bigger compared to the rest, which present general narrow variance. The ‘bone’ modality is heterogeneous and ‘pottery’ y ‘HTS’ modalities are heterogeneous and have wide variance. The boxplot (Figure 9) regarding d3 ostracon/surface shows high variability in all four modalities.
The ‘brick’ modality explains 24 per cent of the variability observed regarding d1, 27 per cent regarding d2 and 10 per cent regarding d3. Then, the variances assigned to the ‘brick’ modality, to the size of the ostraca and to the different modalities of substrate were deducted. After those calculations, there was still 49 per cent of the initial non-assigned variance regarding d1, 48 per cent regarding d2 and 56 per cent regarding d3. For instance, regarding d1, the ‘brick’ modality explains 24 per cent of the variance, and without the ‘brick’ modality, sizes explain 20 per cent of the total variance. All of that together with the analyses of autocorrelation, correlation, variance, normality and the analysis of relative height of letters leads us to reject hypothesis [HC0]. It should be noted that the ‘brick’ modality, which was located in ‘sector 6’, is an internally homogeneous group, which distinguishes from the rest of modalities. The ‘brick’ modality has higher letters and bigger distances between letters in addition to a more variable lead.
These are the variables which are not the cause of the differences observed in the distances; that is, the variables which correlated with distances.
Sector and Stratum
It was observed that the stratum ‘32005A’ and the stratum ‘32005B’ presented normality. Those two strata were different layers of the same profile. Furthermore, in ‘sector 6’, there were various types of ostracon according to distances. All of the brick ostraca were found in that sector. In ‘sector 5’, 4-5 groups of ostraca could be established according to their size.
Theme
Only the ‘anthrophony’ modality was a homogeneous group. The rest of the modalities were internally heterogeneous.
Language
The variability observed regarding each language led us to create distinguished groups with the language factor.
In the analyses carried out, there was still 48 per cent of the variance to be explained regarding d1, 48 per cent regarding d2 and 56 per cent regarding d3. There were four possible scenarios to explain the variance observed, which was not explained by the size of the ostraca or the ‘brick’ modality. It should be noted that part of that variance is residual, that is to say, it is due to randomness and it is not attributable to any factor. It should be noted that this study does not conclude that the suggested scenarios have to be accepted or rejected. Moreover, the possible mixture of the scenarios suggested should be observed. With regard to the non-controlled or unknown variables, this part of the study enumerates the various scenarios that could explain the observations made regarding these 77 ostraca.
As the ostraca have the greatest power to predict the variance, it is interpreted that there were different handwrites regarding the various ostraca. Ostraca found in ‘sector 32’ may be associated with the same handwrite. It may be interpreted that in ‘sector 5’ there are 3-5 handwrites. In ‘sector 6’, there are 2-4 handwrites. In ‘sector 6’, the letters of the two strata are different, what leads us to interpret that there are various handwrites regarding those two strata. Within that sector, the ‘brick’ modality may be associated with one handwrite. In ‘sector 32’, the distances of the various strata do not differ. It may also be interpreted that there is one hand writer regarding the theme ‘antrophonym’, whereas there are several handwriters regarding the rest of the themes. This is the most probable. The fact that there were various handwriters may perfectly explain the groups observed.
With regard to this interpretation, all of the ostraca were made by one or two authors with intent to deceive or to hide their guilt. The author who attempted to deceive was very careful not to be revealed by a forensic study on handwriting, so they changed the size of letters on purpose and created a set of random groups at the entire archaeological site. This intent to deceive is what causes 40 per cent of the variability observed.
That scenario is rather improbable and far-fetched to explain everything observed. Up to now, there is not any type of work on the analysis of handwritten letters on ostraca. Therefore, the faker would have to foresee this study was going to be carried out in order to mask their writing. Moreover, although that same author was very careful regarding handwriting, they were not so careful regarding the themes chosen and the grammar of Latin and Basque.
In this scenario, the cause of the various results obtained is the writing instrument: the punch. Different instruments result in different groups of size of letters, distances between letters and distances between lines. In this scenario, it was predicted that different tools had been used to write on ostraca of different substrates and on ostraca of different strata, which were found in different sectors. The thickness of the point of the instrument may have varied the size of the writing. In that scenario, there would be 4-6 writing instruments at least, which were used in different groups of ostraca: one instrument regarding bricks in ‘sector 6’, another instrument regarding objects which are not made of brick in ‘sector 6’, another instrument in ‘sector 3’ and several instruments in ‘sector 5’.It is difficult to get to the bottom of the reason why using various writing instruments results in different writing sizes on these ostraca. That scenario leads us to think that various handwrites might have used different instruments in different times (strata), what points to Occam’s razor. The alternative is a faker in the team of archaeologists, who works with different instruments on different strata and materials of different times. That is a possible explanation, yet it is very unnatural.
In this study, only 77 ostraca could be analyzed out of approximately 450 with texts written on them. Future studies should include the rest of the ostraca up to 400. A method to measure the distances analyzed more strictly should be studied. In order to achieve a strict and effective definition of the distances, texts should be written on the same material (bones, HTS, brick, pottery), without any value, by using various writing instruments and writing a great number of letters; perhaps up to thousands of them. Defining distances in various ways and then studying the definitions of the distances is essential to minimize biases and errors. That is, determining the effect of different definitions of the distances defined in this study in an experimental way. Furthermore, experiments should be carried out with material of different sizes and with different substrates, writing tools and people. Consequently, the effects of each factor on the size of letters should be studied as well. It should be noted that the quantification of the residual variance which could not be assigned to any factor is an unresolved issue.
The study does not establish whether the substrate is an eliciting variable or a correlated variable with regard to the size of letters and its relationship with the handwrites. What is more, it was not established in which way ‘brick’ may affect the size of letters, if it were an eliciting factor of the large size of letters, taking into account that some bricks had been written on before being baked. The ostraca with the greater amount of letters seem to be more likely to autocorrelated. Nevertheless, it should be studied further. In addition, the individuality of writing on handwritten ostraca was not established with scientific rigor. These days, the hypothesis of individuality of the distances studied must be experimented, structured, tested and peer reviewed. It is necessary to take high resolution photographs under controlled conditions to all texts.
The author of the article appeals to the owner of the ostraca to carry out neutral laboratory analyses of the bones and bricks. That is, carrying out the analysis of letters and various ostraca which hide letters. After 10 years, those analyses have not been carried out yet.
