Forensic Anthropology and Archaeology: Introduction to a Broader View
Bibliographic record
Abstract
This chapter explores an evaluation of the contribution that individual Canadian forensic anthropologists have made and are currently making to death investigation. It defines Canadian content with an emphasis on one's contributions to forensic osteology and fieldwork locally and internationally. The chapter identifies the intellectual lineages of anatomists and biological anthropologists who chose in their careers to engage themselves and their students in specific forensic anthropological concerns victim identification, elapsed time since death, circumstances of recent deaths, and recovering physical evidence of perpetrator behaviors. It examines how and to what extent legal jurisdictions have reached out to academic expertise in one's communities in death investigation. The chapter evaluates the content of publications by Canadians in forensic anthropology and archaeology over a span of several decades. It explains that forensic anthropologists are redefining their roles and their discipline to keep it vibrant and relevant.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".