The Significance of Osteobiographies: : Building a Life Narrative for the Individuals buried in the Royal Navy Hospital Cemetery (1793-1822) in Antigua
Bibliographic record
Abstract
To better understand the past holistically, osteobiographies provide an excellent framework for bioarchaeologists. Through building an osteobiography, many lines of evidence need to be analyzed, such as the mortuary, social and historical context of a site, and skeletal data. This paper looks at multiple lines of evidence required to build an osteobiography, using a cemetery in Antigua as a case study. In Antigua, a site on the English Harbour was excavated from 1998-2001, where 30 suspected naval soldiers were buried. This site was located behind a Naval Hospital that ran from 1793-1822 AD during the Napoleonic Wars. Many researchers have studied this site since 2001, attempting to learn more about the history of this site, considering there was little historical documentation recovered. While using the Antigua cemetery as a case study, I will expand on how different lines of evidence are used to analyze the geographic origins of individuals, skeletal data, and the social, mortuary, and historical context of the site. The objective of this paper is to discuss the significance of osteobiographies to bioarchaeology, while highlighting some of the limitations associated with creating osteobiographies. Department: Anthropology Faculty Mentor: Dr. Treena Swanston
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".