Taking the Pulse of Forensic Anthropology in Canada
Bibliographic record
Abstract
The current state of forensic anthropology is evaluated by a limited cross-section of active practitioners from across Canada. The number of specialists undertaking casework in this area of forensic science is growing, as are expectations of the quality of our work from death investigative agencies nationally. However, the majority of forensic anthropologists in Canada are university-based and inadequately supported by their administrative units in terms of secure lab facilities and finances. Federal and Provincial funding agencies that could support research in this field and assist with creating infrastructure to meet emerging expectations of standards of forensic practice are by and large not doing so. Across Canada, unevenness of standards of training, practice and support reflects a lack of appreciation of forensic anthropology by society as a whole, as well as by funding agencies and the legal community. It also reflects the willingness of individual forensic anthropologists to accept this neglect and under-valuation of their work, likely because of a professional commitment to the task which they accept as a social responsibility. It is recommended that a national consensus of standards of practice, facilities and training be achieved among forensic anthropologists so as to place pressure on federal funding agencies in Canada to support forensic anthropology adequately in the future.
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.034 | 0.010 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".