Bibliographic record
Abstract
Chapter 3 explores the identity strategies that La Fulana and Free Gender have employed in their activism. The chapter puts forward and defines two different identity strategies that organizations employ: commensurability and visibility. The first half of the chapter shows how Free Gender strategizes lesbian identity to be commensurate with other important social and political identities such as “woman,” “African,” and “community member.” Doing so allows Free Gender to advance its goal of eliminating violence against lesbians in their local community. The second half of the chapter shows how La Fulana develops a strategy of lesbian visibility to increase the salience of lesbian identity relative to other social identities. This strategy aims to correct the social and political erasure of lesbians in public that persists after the acquisition of citizenship rights. Overall, the chapter adds to the literature by explaining the kinds of strategies organizations may use when explicitly strategizing multiple identities at once, and how these strategies address the limitations of legally inclusive citizenship.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".