Power in Coalition: Strategies for Strong Unions and Social Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The labor movement sees coalitions as a key tool for union revitalization and social change, but there is little analysis of what makes them successful or the factors that make them fail. Amanda Tattersall—an organizer and labor scholar—addresses this gap in the first internationally comparative study of coalitions between unions and community organizations. She argues that coalition success must be measured by two criteria: whether campaigns produce social change and whether they sustain organizational strength over time. The book contributes new, practical frameworks and insights that will help guide union and community organizers across the globe. The book throws down the gauntlet to industrial relations scholars and labor organizers, making a compelling case for unions to build coalitions that wield "power with" community organizations.Tattersall presents three detailed case studies: the public education coalition in Sydney, the Ontario Health Coalition in Toronto, and the living wage campaign run by the Grassroots Collaborative in Chicago. Together they enable Tattersall to explore when and how coalition unionism is the best and most appropriate strategy for social change, organizational development, and union renewal. Power in Coalition presents clear lessons. She suggests that "less is more," because it is often easier to build stronger coalitions with fewer organizations making decisions and sharing resources. The role of the individual, she finds, is traditionally underestimated, even though a coalition's success depends on a leader's ability to broker relationships between organizations while developing the campaign's strategy. The crafting of goals that combine organizational interest and the public interest and take into account electoral politics are crucial elements of coalition success
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it