Union brokerage and the gender gap in the labor market: A cross-national comparative study of associational networks and gendered labor force participation in OECD countries
Bibliographic record
Abstract
This article explores the role of union-centered brokerage in promoting women’s labor force participation in Organisation for Economic Co-operation and Development countries for the last three decades. Using two measures of brokerage, a union’s core brokerage role, and its general brokerage role, we attempted to capture the processes by which union activists mobilize and extend women’s rights in associational fields. Then, we tested our key argument that union-centered brokerage plays the most effective role among the different brokerage types in channeling women’s interests by transforming them into wider class-linked or cross-class concerns. Cross-national and comparative case studies demonstrate that union-led brokerage promotes greater presence of women in the economy. Our findings revealed that, when controlling for economic, regional, and cultural factors, both types of brokerage roles impact women’s participation in the labor market and their participation compared to that of men. The overall findings underscore the importance of creating and utilizing solidarity structures through effective channeling mechanisms in civic associational fields between labor-based organizations and other reform-oriented civic groups in achieving egalitarian socioeconomic goals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".