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Record W4393018767 · doi:10.7202/1105919ar

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2022· article· fr· W4393018767 on OpenAlexaffabout
Louise Nachet, Sabrina Bourgeois

Bibliographic record

VenueRevue d’études autochtones · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicHydropower, Displacement, Environmental Impact
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComputer scienceData scienceBusiness

Abstract

fetched live from OpenAlex

Cet article propose d’explorer les relations entre les gouvernements, l’industrie minière et les Autochtones lors de la crise sanitaire de la COVID-19 au Canada afin d’identifier quelle est la place accordée aux préoccupations autochtones dans le discours des entreprises et des gouvernements durant les premiers mois de la pandémie de la COVID-19. Les positions des acteurs et les cadres discursifs qu’ils ont déployés au cours de l’année 2020 ont été identifiés et analysés à travers une revue de littérature narrative. Les résultats de cette analyse suggèrent la formation d’une coalition discursive entre les gouvernements et l’industrie minière lui permettant de se placer en acteur incontournable de la relance économique ; et la marginalisation relative des revendications et perspectives autochtones.

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)0.004
metaresearch head score (Gemma)0.015
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0130.008
Scholarly communication0.0110.009
Open science0.0020.007
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0820.025

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 head0.051
GPT teacher head0.386
Teacher spread0.335 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueRevue d’études autochtonesSame topicHydropower, Displacement, Environmental ImpactFrench-language works237,207