Lobbying Beyond the Legislature: Challenges and Biases in Women's Organizations’ Participation in Rulemaking
Bibliographic record
Abstract
Abstract This study, which is based on a survey of women's organizations’ staff members, answers two previously unexamined questions about women's groups’ participation in the rulemaking process: (1) How do women's organizations participate? (2) What are the characteristics of the women's organizations that are the most likely to participate? About one-quarter (27%) of women's organizations reported that they lobby rulemakers, often using relatively low-cost methods, such as submitting comments or signing on to comments written by coalitions or like-minded groups. Women's organizations with large staffs that are structured the most like political insiders or influential economic interest groups were the most likely to participate in the process, potentially biasing participation in favor of relatively advantaged subgroups of women. Together, these results suggest that although rulemaking presents unique opportunities to represent women, the most marginalized women may be underrepresented during rulemaking debates.
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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 | 0.075 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".