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Record W4396562379 · doi:10.1080/11926422.2024.2344001

Overlooking nature: the Arctic, climate change, and environmental diplomacy in the study of Canadian foreign policy

2024· article· en· W4396562379 on OpenAlexaffabout
Wilfrid Greaves, Gabriella Gricius

Bibliographic record

VenueCanadian Foreign Policy Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDiplomacyForeign policyScholarshipPolitical scienceArcticClimate changeForeign policy analysisMainstreamInternational relationsPoliticsLawEcology

Abstract

fetched live from OpenAlex

This article examines three prominent concepts in post-Cold War foreign policy in Canada: the Arctic, climate change, and environmental diplomacy. We study the prevalence of articles on these topics in the wider field of Canadian foreign policy studies, who is responsible for their production, and how they relate to each other and to the broader field of Canadian foreign policy studies. Through a quantitative analysis of these concepts in six academic journals between 1989 and 2022, we find that contrary to their importance to Canadian foreign policy practice during that time, the Arctic, climate change, and environmental diplomacy are: (1) marginal to mainstream scholarship on Canadian foreign and security policy; (2) conceptually closely linked together; and (3) shaped by the knowledge production of a relatively small epistemic community of scholars. We outline the methodology of our literature review for the Arctic, climate change, and environmental diplomacy within Canadian foreign policy scholarship, present our findings, and discuss their significance for understanding these topics within the broader field of Canadian foreign policy studies.

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)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.560

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0140.028
Science and technology studies0.0190.022
Scholarly communication0.0170.005
Open science0.0010.004
Research integrity0.0020.002
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 head0.030
GPT teacher head0.308
Teacher spread0.278 · 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 designTheoretical or conceptual
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

Citations3
Published2024
Admission routes2
Has abstractyes

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