Calling all fag hags: from identity politics to identification politics
Bibliographic record
Abstract
This paper looks beyond “identity politics” through the figure of the fag hag. In the “identity politics” of the 1980s and 1990s, a person's politics were based solidly in what one identified as: straight woman, gay man, Asian American, and so on. Who one identified with, it was presumed, was—or should be—identical to what one identified as. This kind of identity politics, which was very productive in effecting important social change, has now reached an impasse. Groups of people identifying differently cannot seem to find common ground on which to work together collectively, leaving the political left divided, unable to collect into a mass force. There is now a felt need for a new kind of identity politics that moves beyond this impasse. Meanwhile, the fag hag has been gaining visibility, popularity, and even respectability. What defines the fag hag is not—or not only—what she identifies as (usually, straight woman), but more importantly who she identifies with (gay men). Further, she embodies the possibility and pleasures of a radical disjunction between identifying as and identifying with. A new identity politics that can get beyond the impasse of “positivist” identity politics will follow the fag hag's lead in validating an identifying with distinct from an identifying as—and indeed, in relishing the dialectic between identifying as and identifying with.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.076 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".