New Opportunities, Rising Constraints: Assessing the Gendered Opportunity Structure of Myanmar’s Resistance Movement
Bibliographic record
Abstract
Abstract In a global context of rising right-wing governance, feminist and gender equality actors are facing the reversal of hard-won gains and the deterioration of what they view to be the conditions for advancing gender equality. While these shifts are not failures of feminism, they do represent devastating setbacks that fundamentally challenge the strategies and tactics of gender equality movements. In response, gender equality actors are joining with a range of others in broad resistance coalitions, often with regime change goals. Although this is a common strategy of gender equality actors under similar political conditions, scholars do not typically analyze how joining with these broad coalitions impacts opportunities to work on gender equality. In other words, it is not common to examine within-movement opportunity structures and how gender equality actors are impacted by them. To contribute to addressing this gap, in this article I build on the concept of “gendered opportunity structure” to analyze the within-movement gender dynamics of Myanmar’s resistance movement and how gender equality actors are responding to them. My findings indicate that, while the current gendered opportunity structure of the movement provides some openings for the expanded participation of individual well-established gender equality actors and gender-diverse people, critical shifts in the within-movement opportunity structure have had the net effect of narrowing the objectives and strategies of gender equality actors. Via this case analysis, I propose that the concept “within-movement gendered opportunity structure” may be generative in helping scholars and practitioners better think about how gender equality actors are enabled and constrained by joining heterogeneous resistance movements. This concept, along with the suggested framework and model of analysis worked through in this article, could be adapted for analysis of other relevant cases. More consistent casework on the experiences of gender equality actors within broad-based resistance movements would help build knowledge about the conditions under which resistance movements of this kind may support gender equality outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".