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Record W6991863107

Indian Day Schools in Michi Saagiig Anishinaabeg Territory, 1899-1978

2021· dissertation· en· W6991863107 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGovernment (linguistics)Context (archaeology)DutySituatedAdministration (probate law)TreatyFiduciary
DOInot available

Abstract

fetched live from OpenAlex

This dissertation examines the history of Curve Lake Indian Day School between, 1899-1978 situated in the larger context of Indigenous education in Ontario during the first three quarters of the 20th century. Drawing on a collection of over 10,000 primary sources from the federal government, newspapers, and local archives the dissertation demonstrates how the federal government failed to recognize inherent treaty rights and fulfill its fiduciary duty towards education with Curve Lake First Nation. The federal government–through the administration of the Methodist and United Church–significantly underfunded the schools in comparison to the public system in Ontario and banned the use of Indigenous languages. This racially segregated system provided inadequate and poorly educated teachers, that caused significant issues among community members including abuse, loss of language, and dangerous health conditions. This pattern of mismanagement is still impacting their descendants today, especially with their health and revitalization of the traditional language: Anishinaabemowin. Using the method of “two-eyed seeing” this research was guided and informed by Curve Lake First Nation ensuring that all the historical data examined was easily accessible by the community through its elected Band Council under the leadership of Chief Emily Whetung.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueQSpace (Queen's University Library)→Same topicIndigenous Health, Education, and Rights→French-language works237,207→