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Record W6950047507 · doi:10.5281/zenodo.3775686

Identifying and documenting the locations of Indian residential schools in Canada

2018· article· en· W6950047507 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsGeospatial analysisWork (physics)CommissionBaseline (sea)Geographic information system

Abstract

fetched live from OpenAlex

Between 1851 and 1998, over 139 Indian residential schools operated across Canada. The legacy of these schools is the trauma that has left scars in the communities in which the schools resided, as well as for school survivors and their descendants. The precise location of the schools is known for many of them, but this has yet to be determined for the rest. This poster will discuss the methodology, challenges, and outcomes of a project currently underway, in collaboration with the National Centre for Truth and Reconciliation (NCTR), to continue the work of the Truth and Reconciliation Commission and its allies, by finding and documenting the precise locations of these school buildings and properties. The goal is to produce a geospatial dataset and interactive map of residential school locations that shows the location of the primary school buildings; and a bibliography of maps, plans, aerial photographs, and unpublished documents that show the location of school buildings and properties. The data and bibliography will assist the NCTR and their partners in identifying and documenting the location of residential school cemeteries and unmarked graves, as well as assist educators, students, researchers, elders, and survivors in telling their own stories.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.040
GPT teacher head0.240
Teacher spread0.200 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2018
Admission routes2
Has abstractyes

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