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Record W2808291337 · doi:10.1111/cag.12472

Canadian geopolitical culture: Climate change and sustainability

2018· article· en· W2808291337 on OpenAlexafffundvenueabout
Simon Dalby

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsWilfrid Laurier UniversityBalsillie School of International Affairs
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeopoliticsRhetoricClimate changeGovernment (linguistics)Political scienceSustainabilityNationalismPolitical economyEnvironmental ethicsPublic administrationSociologyLaw

Abstract

fetched live from OpenAlex

Key Messages Canadian nationalist rhetoric is still premised on resource extraction despite discussions of climate change by the Liberal government since 2015. Taking earth system science seriously requires rethinking national identity and the conventional geopolitical premises on which it is based. Meeting Paris Agreement on Climate Change commitments requires much more fundamental changes than Canadian Federal government rhetoric so far acknowledges.

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)0.002
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.122
Threshold uncertainty score0.887

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0200.016
Scholarly communication0.0090.004
Open science0.0020.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0240.001

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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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

Citations23
Published2018
Admission routes4
Has abstractyes

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