Lesbian visibility and the politics of covering in women’s basketball game spaces
Bibliographic record
Abstract
In this article, I use research on the lesbian fans of US women’s professional basketball (WNBA) to outline how a set of exclusive cultural politics (re)produces a curious form of self‐regulation amongst target consumers. I link leisure geographies and geographies of sexuality through an analysis of lesbian visibility to examine the ways that identity performance is shaped by the implicit cultural politics at work in WNBA arenas. I use Kenji Yoshino’s adoption of Erving Goffman’s term ‘covering’ to discuss the ways that normative ideologies are reinforced by management‐driven practices and by the self‐circumscribing practices of some lesbian fans. I show that covering is noteworthy as both an effect of marginalisation and as a mandate that encourages lesbian fans to reproduce the dominant discourse at work in WNBA arenas. I argue that act of covering illustrates the material effects of normative power relations and the ways that these effects are understood to be a natural outcome of an apolitical economic logic instead of the result of the decidedly political process of spatial production. I contend that attempts to cover give lesbian fans a false sense of power to regulate the reception of their bodies and their enactments of normativity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".