When Provenience Is Lost: Achievements and Challenges in Preserving the Historical St. John's, Belize, Skeletal Collection
Bibliographic record
Abstract
Abstract In small developing countries like Belize, lack of funding for archaeological research and post excavation curation remains one of our greatest challenges to preserving our tangible cultural heritage. The state of curation of human remains and artefact collections at St. John's College in Belize City is a perfect example of what can go wrong in the absence of a properly funded and managed curation program both at the national and the institutional level. This article highlights the rediscovery of a historically significant group of over 70 human remains in the biological collection of Friar Deickman, which had been forgotten in an attic after his death in 2003. We outline the process of, and accomplishments in improving the curation conditions of these individuals while uncovering their importance to Belizean history in the eighteenth through twentieth centuries. Preliminary analysis reveals life histories of slavery and indentured servitude of individuals of African, Maya, European, and possible mixed African and European descent. We emphasize the importance of ethical responsibility in properly curating excavated human remains, and the challenges researchers face when poor curation results in lost provenience. We offer suggestions for scientific analysis in recovering information lost as a result of poor excavation or curation methods.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".