Guest editors’ introduction: Contending with gendered contradictions and blind spots in Canadian foreign policy
Bibliographic record
Abstract
Despite the extensive and multisectoral scholarship on Canada's approaches to gender equity in its foreign policy, over two decades have passed since the publication of the last volume on the subject. Mindful of this gap and of retrenched state commitments to gender equity amid a return to more “traditional” diplomatic and security concerns and intransigent antagonism to justice and equity-driven agendas, contributors to this special issue analyze feminist foreign policy in Canada to assess who the policies and rhetoric benefit, how they are conceptualized, and where they are located. The contributions speak to the paradoxes of Canada's feminist foreign policy through the themes of contradictions and blind spots. Canada's renewed commitments to militarism, the instrumentalization of women's representation in the Canadian Armed Forces, and the strategic absence of feminist rhetoric in relation to the war in Ukraine demonstrate the many contradictions of Canada's approach, while explorations of disability, migration, and post-secondary education showcase its blind spots. By emphasizing these contradictions and blind spots, the scholarship in this special issue critiques the contemporary gendered realities of Canada's global policies and actions to imagine alternatives to the status quo and the potential of emancipatory and wholistic feminist foreign policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".