Feminist foreign policies (FFPs) as strategic narratives: Norm translation in Sweden, Canada, France, and Mexico
Bibliographic record
Abstract
Abstract Drawing on the IR theories of norm translation and strategic narratives, this article focuses on how states translate international norms to their own advantage by producing strategic narratives to advance their soft power ambitions abroad. Using the example of feminist foreign policy (FFP), the article compares Sweden, Canada, France, and Mexico in their attempts to translate international feminist norms into their countries’ strategic narratives. This comparison is based on three strategic narrative types (issue, national, and international system narratives) and two types of feminism (liberal, intersectional). Issue narratives reveal that Sweden and Mexico give more priority to social policies, while France and Canada emphasise the role of the market in addressing gender inequality. International system narratives demonstrate that Sweden and Mexico perceive global challenges as drivers of gender inequality, while France and Canada see gender inequality as a cause of global problems. National narratives show that Sweden and Mexico refer to other FFP countries to ‘back up’ their feminist initiatives, while France and Canada do not relate to other states. Finally, while liberal feminism dominates all four FFPs, each state either prioritises particular aspects of it (legal, market, security, rights-based) or incorporates elements from intersectional feminism.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".