The Politics of Visibility: Coming Out and Individual and Collective Identity
Bibliographic record
Abstract
The theory and practice of social movements come together in strategy—whether, why, and how people can realize their visions of another world by acting together. Strategies for Social Change offers a concise definition of strategy and a framework for differentiating between strategies. Specific chapters address microlevel decision-making processes and creativity, coalition building in Northern Ireland, nonviolent strategies for challenging repressive regimes, identity politics, GLBT rights, the Christian right in Canada and the United States, land struggles in Brazil and India, movement-media publicity, and corporate social movement organizations. Contributors: Jessica Ayo Alabi, Orange Coast College; Kenneth T. Andrews, U of North Carolina at Chapel Hill; Anna-Liisa Aunio, U of Montreal; Linda Blozie; Tina Fetner, McMaster U; James M. Jasper, CUNY; Karen Jeffreys; David S. Meyer, U of California, Irvine; Sharon Erickson Nepstad, U of New Mexico; Francesca Polletta, U of California, Irvine; Belinda Robnett, U of California, Irvine; Charlotte Ryan, U of Massachusetts–Lowell; Carrie Sanders, Wilfrid Laurier U; Kurt Schock, Rutgers U; Jackie Smith, U of Pittsburgh; Suzanne Staggenborg, U of Pittsburgh; Stellan Vinthagen, U West, Sweden; Nancy Whittier, Smith College. Source: Publisher
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.029 | 0.029 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| 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".