Bibliographic record
Abstract
Paying close attention to the Internet has revelatory potential for femme theory. Femme, a queerly feminine sexual and gender identity, has so far been under theorized and is often treated as unimportant or even suspect in queer and feminist studies (Martin, 1996; Harris and Crocker, 1997; Maltry and Tucker, 2002; Dahl, 2017). Work on femme has proliferated in response to this (mis)treatment of femme (Volcano and Dahl, 2008; Rose and Camilleri, 2002; Harris and Crocker, 1997; Duggan and McHugh, 1996; Nestle, 1992b), and looking to the Internet reveals a rich tradition of femme theorizing. In this paper I argue that femme theory is often produced through cultural and community forms and emphasize the potential of blogs and social media as sites of this knowledge production. Femme theory found online challenges the masculinist standards of queerness and, I argue, the masculinist standard of inquiry. I rely on a range of feminist, cultural, and queer theorists who engage with theories of epistemology to shift our understanding of the concept “theory” itself in order to make space for femme epistemology. In addition to challenging the superiority of masculinity, hegemonic femininity and patriarchal gender roles, and defying stereotypes about femmes, femme theory also complicates several aspects of formal knowledge production. Looking to the Internet is a crucial way to locate femme knowledge and attend to gaps in feminist and queer theory.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".