New Dimensions of Global Feminist Influence: Tracking Feminist Mobilization Worldwide, 1975–2015
Bibliographic record
Abstract
Abstract Feminist mobilization, crucial for advancing women's human rights, has increased in all world regions since 1975. However, we do not know enough about the global impact of this mobilization because we lack adequate databases to explore the ways that feminist mobilization interacts with other factors that enhance and limit women's rights, such as democracy, intergovernmental processes, and transnational, regional organizing. Our ability to explore these questions is obstructed by a lack of data on the global south and measures that focus on formal organizations. This project remedies these gaps, developing an improved measure of feminist mobilization that encompasses autonomous, domestic feminist mobilization in 126 countries, 1975–2015, enabling us to track global and regional trends. Using regional comparisons and statistical analysis, we use this new measure to reveal new patterns and complexities in feminist mobilization. We discern distinct regional patterns in such organizing that defy facile predictions of global convergence and suggest a central role for UN processes advancing women's rights. Our analysis also points to the importance of transnational feminist networks and democratization as factors enabling and strengthening feminist mobilization. We conclude by suggesting some fruitful avenues for exploring relationships between feminist movements, international institutions, and democracy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".