An Ethical, Cultural and Historical Background for Cemetery-Based Human Skeletal Reference Collections
Bibliographic record
Abstract
OPEN ACCESS-PAID-CC BY-NC-ND In historically Protestant countries, human skeletal reference collections curated by research institutions have been amassed from bodies dissected by anatomists, typically unclaimed cadavers from morgues and hospitals, or from remains donated to science. In contrast to these anatomy-based and donation-based collections, skeletal reference collections in historically Roman Catholic countries on mainland Europe and in Latin America are for the most part derived from unclaimed remains exhumed from modern cemeteries and ossuaries at the end of the mandated interment period. While much has been written in English about the history, context and ethical framework of anatomy-derived collections, cemetery-based collections have received very little critical attention. The current paper addresses this gap, with particular reference to cemetery-derived collections in Portugal. The cultural and historical context of southern Europe is discussed, particularly Roman Catholic mortuary traditions and the influence of the Napoleonic Code, and these provide the background for an overview of the ethical issues raised by cemetery-derived collections. Here, general principles that should guide the work of human osteologists working in archaeological contexts are relevant, as regards consent, dignity and respect and benefits to science and education, because unlike their anatomy-derived counterparts, cemetery-based collections include individuals who were once buried.
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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 | 0.046 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.011 |
| 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".