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Record W4399095690 · doi:10.1177/00207020241256001

Democratic Identity, Political Policing, and the Other side of the US-Canada Security Community

2024· article· en· W4399095690 on OpenAlexfundaboutno aff
Daniel Troup

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaQueen's UniversityGovernment of Ontario
KeywordsPoliticsDemocracyIdentity (music)Political scienceSociologyPublic administrationPolitical economyLaw

Abstract

fetched live from OpenAlex

Canada and the US maintain one of the closest international partnerships in the world, but there are concerns about its future. Donald Trump's election generated anxieties about the rise of autarkic or authoritarian politics in the region. These worries persist as it remains to be seen whether the Trump administration was a temporary interruption to liberal governance or a signifier of more profound change. Canadian security scholars have contemplated whether an illiberal shift may disrupt the US-Canada security community. This article intervenes in these discussions by emphasizing the extent to which prejudices and political policing have fostered the community's emergence and operation. Archival investigation reveals that illiberal practices have proven reconcilable with a liberal identity throughout the community's development. Therefore, without dismissing existing concerns, we should be vigilant of the possibility that the security community may be internalized within democratic retrenchment, rather than corroded by it.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.100
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0600.075
Scholarly communication0.0190.004
Open science0.0020.010
Research integrity0.0020.006
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 head0.009
GPT teacher head0.320
Teacher spread0.311 · 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
Published2024
Admission routes2
Has abstractyes

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