The Past, Present, and Future(s) of Feminist Foreign Policy
Bibliographic record
Abstract
Abstract Almost a decade after Sweden first declared that it would follow a feminist foreign policy (FFP), a further eleven countries from across Europe, North and South America, and North and West Africa have adopted, or have signaled an interest in potentially adopting, an FFP in the future. These developments have been accompanied by a growing body of feminist scholarship. Although still in its infancy, this literature can generally be divided between more normative accounts and those that are empirically focused, with particular attention paid to the FFPs of Sweden and Canada. Yet, few studies compare FFPs’ uptake across different countries and regions, examine its connections to longer histories of ideas around women and gender, or unpack the policy intersections FFP (tentatively) engages. Contributing to these different areas, Part I provides an overview of the history of FFP, interrogates FFP in the context of Foreign Policy Analysis, and explores what FFP can achieve in the current (liberal) global system. Part II turns to consider policy intersections in relation to the climate crisis, migration, militarism, and bodies. Thinking through its origins, policy intersections, and potential future(s), the contributors to this Forum explore FFP's multiple and contested future(s). Ultimately, the Forum takes stock of this feminist turn in foreign policy at a critical point in its development and considers what future possibilities it may hold.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".