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Record W4390079968 · doi:10.32316/hse-rhe.2023.5193

Digitally Mapping the Indian Day Schools and the RG10 School Files Series in Canada

2023· article· en· W4390079968 on OpenAlexaffvenueabout
Benjamin Farmer Lacombe, Jackson Pind

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsTrent UniversityQueen's University
Fundersnot available
KeywordsSeries (stratigraphy)Mathematics educationGeographyPsychologyGeology

Abstract

fetched live from OpenAlex

This article explores the process of creating a digital history resource on the topic of the Indian Day School system in Canada. It provides an overview of how the website www.indiandayschools.org was created by a team of history educators who built on the work and legacy of Indigenous activist Raymond Mason. The new open-access, inquiry-based resource displays all 699 recognized Indian Day Schools on an interactive map and integrates thousands of optimized files from Library and Archives Canada’s RG10 School File Series. This article details the challenges we encountered while undertaking this project and how digital Indigenous history can be a powerful tool for reconciliation.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.479

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.029
Science and technology studies0.0080.003
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.066
GPT teacher head0.294
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2023
Admission routes3
Has abstractyes

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