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Record W2765274876 · doi:10.5663/aps.v6i2.28227

Rejecting the Standard Discourse on Métis Lands in Manitoba

2017· article· en· W2765274876 on OpenAlexaffvenueabout
D’Arcy Vermette

Bibliographic record

Venueaboriginal policy studies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMetisInterpretation (philosophy)ScholarshipGovernment (linguistics)Field (mathematics)LawSociologyHistoryPolitical scienceLinguisticsPhilosophyComputer science

Abstract

fetched live from OpenAlex

This paper challenges the assumptions and interpretative frameworks used by several prominent scholars of Metis history in Manitoba. The scholars were chosen for their prominence in their field and their particiation as expert witnesses in recent litigitation betwen the Crown and the Manitoba Metis. The article demonstrates that the scholarship (dubbed the "standard discourse") is repleat with apologists techniques meant to undermine the impact of government blundering, unsound historical interpretation, an emphasis on individuals as the unit of interpretating history and, in the case of one author, a well-established bias against Aboriginal cultures. The paper serves as a window into understanding the unspoken aspects of the standard discourse. Utimately, it is helpful to recognize that much of the standard discourse is based on preference of the researcher and does not represent an absolute truth in interpretation.

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)0.008
metaresearch head score (Gemma)0.010
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.928

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0240.041
Scholarly communication0.0090.003
Open science0.0020.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.527
Teacher spread0.434 · 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
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
Published2017
Admission routes3
Has abstractyes

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