Bibliographic record
Abstract
Chapter 4 considers dilemmas that arise for “successful” LGBT movements with increasing access to and interactions with state bureaucracies. The chapter applies an intersectional lens to neoliberal inclusion to reveal how inclusion along one dimension (sexuality) may constrict organizations along other dimensions (access to resources), influencing the ability of organizations to deploy their identity strategies. The chapter first examines how, in Argentina, activists who took up salaried positions in the bureaucracy were able to deploy their strategy of lesbian visibility from within the state to advance pro-LGBT public policy. However, activists’ engagement with the state weakened the organization and compromised its ability to deploy its identity strategy in the public sphere. The chapter then contrasts this example of state engagement with Free Gender’s decisions in South Africa. Free Gender declined to participate in a major national initiative and chose instead to engage with local police and deploy its identity strategy in these interactions. The chapter concludes by drawing lessons about the consequences of neoliberal inclusion on LGBT organizations, specifically how it may limit their potential to effect change regardless of the choice organizations make to engage the state or not.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.080 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".