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Record W6986246146

A Painful Legacy : A Critical Discourse Analysis of Canadian Government Discussions on Residential Schools

2022· other· en· W6986246146 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGovernment (linguistics)Nexus (standard)Critical discourse analysisColonialismDiscourse analysisLanguage policyIndigenous language
DOInot available

Abstract

fetched live from OpenAlex

Indigenous language rights in Canada have been historically marginalized, with the residential school system being integral to their attempted erasure. These schools stripped indigenous children away from their homes to undergo forced assimilation grounded by colonial language policy which saw the indigenous peoples as impediments to their progress. The schools have since closed and the Canadian government has apologized for their role, deeming them as part of the nation's grim history. This paper explores how this shift in discourse occurred and what it says of Canadian language policy. Using critical discourse analysis, nexus analysis, and language management theories to analyze historical government data, the results find that initial shifts in discourse were superficial, while more contemporary discourse marked a unanimous shift away from past ideology. Colonial language policy was discontinued with this stark discursive change, yet despite vocal support for indigenous languages rights, the long lack of actual policy suggests that lingering colonial legacies of language values may be firmly rooted in Canadian society.

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)0.011
metaresearch head score (Gemma)0.023
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 score0.162
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0110.019
Science and technology studies0.0510.032
Scholarly communication0.0170.006
Open science0.0030.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.305
Teacher spread0.283 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueKTH Publication Database DiVA (KTH Royal Institute of Technology)French-language works237,207