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Record W4413303728 · doi:10.1080/11926422.2025.2545256

A balancing act: Canada's multilateral engagement in the Israel–Palestine conflict

2025· article· en· W4413303728 on OpenAlexaffabout

Bibliographic record

VenueCanadian Foreign Policy Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsPalestinePolitical sciencePolitical economyAncient historyHistorySociology

Abstract

fetched live from OpenAlex

This paper examines Canada's evolving engagement in the Israel-Palestine conflict as a lens to interrogate the interplay between like-minded and principled multilateralism in Canadian foreign policy. Situating the analysis within middle power theory and the post-World War II multilateral tradition, it advances the concept of selective multilateralism-a strategically reactive posture that reconciles normative aspirations with the structural constraints of alliance politics. Through a historical and contemporary analysis spanning 1947 to the aftermath of the October 7 Hamas attacks, the study traces how Canada's positions have oscillated between declarative support for international law and practical alignment with Western bloc priorities. Drawing on UN voting records, official statements, and policy episodes under successive governments, it demonstrates that while moments of principled action-such as arms embargoes or support for humanitarian mechanisms-persist, they are frequently offset by abstentions, rhetorical hedging, or reluctance to hold allies accountable. The post-October 7 period, marked by intensified geopolitical polarization, reveals both the capacity and the limits of selective multilateralism in sustaining Canada's credibility as a bridge-builder. The paper concludes that Canada's future relevance in multilateral diplomacy will hinge on its ability to pair legal consistency with credible strategic engagement, even when doing so entails political and alliance costs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2025
Admission routes2
Has abstractyes

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