Introduction: (Re)integrating Feminist Security Studies and Global Political Economy: Continuing the Conversation through Empirical Perspectives
Bibliographic record
Abstract
Attempts to integrate feminist security studies (FSS) and feminist global political economy (GPE) were first meticulously studied in the Critical Perspectives sections of the June 2015 and December 2017 issues of this journal. Although the debate has gained presence in workshops, at international conferences, and even on dedicated websites, the diverse contributions have remained rather theoretical (e.g., Bergeron, Cohn, and Duncanson 2017; Hudson 2015). The aim of these Critical Perspectives essays is to take the integration of FSS and GPE one step further by presenting empirically grounded contributions that help us contextualize the existing theoretical debates. By focusing on postwar contexts, the pieces here take seriously the material conditions of women's empowerment from a perspective attuned to the gendered and racialized logics structuring social orders in postwar states. We believe that these are the spaces where war economies and peace economies meet and where (gendered) structural transformation of societies is possible. Like the two previous collections, we do not understand FSS and GPE as additive (Chisholm and Stachowitsch 2017). Rather, we understand them as traditions that share a common goal, namely, to undermine the racialized neoliberalism and patriarchal capitalism underpinning international intervention and postwar reconstruction projects.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 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".