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Record W4387957937 · doi:10.1002/arp.1914

Community‐led investigations of unmarked graves at Indian residential schools in Western Canada—overview, status report and best practices

2023· article· en· W4387957937 on OpenAlexaffabout
Brian M. Whiting

Bibliographic record

VenueArchaeological Prospection · 2023
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsPhoenix Technologies (Canada)University of British Columbia
Fundersnot available
KeywordsIndigenousNeglectAttendanceGovernment (linguistics)Residential schoolGeographyPolitical scienceSocioeconomicsHistoryEconomic growthSociologyLawMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.013
Science and technology studies0.0110.004
Scholarly communication0.0040.001
Open science0.0040.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.060
GPT teacher head0.326
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2023
Admission routes2
Has abstractyes

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