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Record W2964184115 · doi:10.26575/daj.v32i2.296

Interpreting the oral condition in medieval European populations from a bioarchaeological perspective

2019· article· en· W2964184115 on OpenAlexaff
Katie Zejdlik, Jonathan D. Bethard, Zsolt Nyárádi, Andre Gonciar

Bibliographic record

VenueDental Anthropology Journal · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsOutotec (Canada)
Fundersnot available
KeywordsStandardizationPerspective (graphical)Interpretation (philosophy)Oral healthHistoryOral cavityBioarchaeologyPathologicalGeographyAnthropologyArchaeologyMedicineSociologyDentistryPathologyLinguisticsArtPolitical scienceVisual artsLawPhilosophy

Abstract

fetched live from OpenAlex

Interpretation of dental ‘health’ in archaeologically derived skeletal assemblages is challenging due to the lack of patient histories, clearly understood pathological processes, broad etiologies, and cultural perceptions of health. Furthermore, the language used in description of pathological conditions of the oral cavity condition is not consistent across researchers thereby resulting in challenging cross-site comparison. Standardization of terms and description is necessary as proposed by Pilloud and Fancher (2018). This paper demonstrates the challenges associated with cross-site comparisons through an attempt to place medieval Transylvanian Székely peoples’ oral status within a larger medieval cultural and biological framework. To do this, first, a review of medieval perceptions of dental health and treatment is provided. Next, a total of 90 individuals recovered from two medieval Székely cemeteries were analyzed for age, sex, and pathological conditions of the oral cavity. The results of the analysis were then compared to other medieval skeletal assemblages reporting on dental ‘health’. Finally, a discussion of how the Székely compare to other medieval sites and the challenges faced are presented thus supporting Pilloud and Fancher’s (2018) call for standardization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.035
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.314
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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