Feminist Scholar-Activism Goes Global: Experiences of “Sociologists for Women in Society” at the UN
Bibliographic record
Abstract
This article focuses on the experiences and strategies of members of Sociologists for Women in Society (SWS) who strive to bridge the worlds of social activism and academia. It concerns the International Committee’s work at the United Nations (UN), specifically at the annual Commission on the Status of Women (CSW) meeting. It builds on transnational feminist literature that has discussed the UN as stage for a diverse global women’s movement and provider of global gender equality norms that, if utilized, advance gender equality in its member states. I analyze themes that emerged from a sample of in-depth interviews with current or former UN scholar-activists within SWS from a larger ethnographic study, and present experiences and challenges of SWS members’ engagement with UN politics and policy development since the mid-nineties. I demonstrate that SWS does justice to its mission of serving as an activist organization through its work in the global arena. Analysis of interviews, observations, and archival material demonstrates that SWS’s UN scholar- activism is increasing the visibility and applicability of feminist sociology. While this activism critically examines the discourse, it also disrupts hegemonic discourse and offers opportunities for concrete social change, particularly through linking activism, mentoring, and teaching.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.031 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".