Diversity and Equity from Below: Media Worker Unions and Collective Bargaining
Bibliographic record
Abstract
This article examines how media workers tackle equity and diversity from below via collective bargaining. In a review of 69 contracts negotiated during the new media union movement’s most active years (2015–2022), we find that collective bargaining is a vital strategy for meaningfully addressing equity and diversity in media. Collective agreements directly address structural causes of inequity in media organizations, articulate solutions, outline specific ways management must remedy problems, and include mechanisms to monitor progress and hold employers to account. Notably, in the contracts we analyze, language on racial, gender, and sexual equity is explicit and implicit. Some clauses directly address equity, such as discrimination and sexual harassment, and others do so indirectly, via salary minimums and flexible leaves, for example. Although individual contracts vary in their attention to equity and diversity, we argue that overall media workers’ commitment to equity is diffuse throughout our corpus of contracts. Negotiated contracts to date establish and encode equitable workplace protections that challenge the conception of a generic, white, male media worker that has historically influenced union bargaining priorities and shaped newsroom experiences. These findings are significant because they demonstrate that worker-led, collective processes of unionization and bargaining can materially and meaningfully address equity and diversity in media.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".