Women’s Gain: Fund-Raising and Fund Allocation as an Evolving Social Movement Strategy
Bibliographic record
Abstract
Formal social movement organizations became significant phenomena in the late 20th century. This article is part of a large national study that examines the historical evolution of one such organization, the Women’s Funding Network (WFN), which is connected to an “industry” of social change funds. WFN includes over 70 women’s funds across the United States and Canada that define fund-raising and fund allocation as a strategy for empowering women and achieving social change. Using the resource mobilization framework of Zald and McCarthy, the author considers critical issues that women’s funds have faced since their emergence as a network (1985) dedicated to social movement goals. Examples from case studies, surveys, and participant observation are used to compare individual funds and to analyze the impact of increasing institutionalization on mission, structure, resource mobilization, leadership, and programmatic activities of WFN as a social movement organization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".