Community‐led investigations of unmarked graves at Indian residential schools in Western Canada—overview, status report and best practices
Bibliographic record
Abstract
Abstract Part of Canadian history that is now being addressed is the legacy of Indian residential schools (IRSs) and closely related institutions. For most of their 200‐year‐plus history, these were run by various churches or religious organizations, and many were directly funded (and eventually run) by government. Attendance by Indigenous children at these schools was made compulsory, and children were deliberately taken far from their cultural base, native language and family in the name of cultural assimilation. Abundant and longstanding evidence has documented abuse, neglect and high rates of death at the schools. Most or all schools had cemeteries, many of which have fallen into neglect and/or been lost through time. Documenting the numbers, names and burial locations of students who died at the schools has become a national priority. Since 2021, interest in this work has accelerated, due in large part by media announcements of geophysical findings of potential unmarked graves at various school sites. Geophysical surveys for unmarked graves are planned or underway at a large number of school sites nationwide. Related lines of research are seeking to document the extent and nature of student deaths based on archival records, survivor accounts and other lines of evidence. As suggested by government and demanded by Indigenous communities, these searches are being led by the affected communities. This paper represents a snapshot of elements of the work in progress, based in part on the personal participation of the author in multiple IRS searches and resulting direct involvement with local communities. Included in this contribution are a historic context, broad overview of community participation/leadership and suggested refinements to geophysical survey best practices that have been promulgated by the Canadian archaeological community and other nationwide organizations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".