Strategies to support equality bargaining <i>inside</i> unions: Representational democracy and representational justice
Bibliographic record
Abstract
The article examines two internal union strategies for improving equality bargaining. The first, representational democracy (RD), highlights the demographic profile of women’s participation in collective bargaining (CB). The discussion presents the existing, albeit imperfect, data on women’s participation. It supports the continuing importance of the gender profiles of negotiators, but also considers the limits of RD via an exploration of essentialism, critical mass and gender composition. It concludes that RD is a limited proxy for voice, and, given the individualism inherent in its claims, an imperfect vehicle for collective agency. The paper then develops the concept of representational justice (RJ), which speaks to collective mechanisms which ensure that women’s interests are represented; in effect, a move from individual equality champions to vehicles for championing equality. As one means to such an end, the article argues for building formal and constitutionalized links between CB and union equality structures. Highlighting internal union strategies to support equality bargaining complements the widespread focus on the substantive issues on the bargaining agenda and takes the discussion of equality bargaining in new directions. Certainly, this approach underscores the importance of unions linking struggles around diversity, equality and representation inside unions to the CB process and agenda.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".