Bibliographic record
Abstract
When I was in graduate school in the second half of the 1980s, there was no queer theory, or at least not by that name. There was a field called Gay and Lesbian Studies—not that the name was official—but there were not that many scholars who worked in it and it had no official standing in the academy. My doctoral thesis was on Christopher Marlowe and I am pleased to say that it had substantial gay content (or homoerotic content or same-sex content; there was a plethora of terms and no agreement on them). When I was nearing the end of my thesis, I gave a very gay paper on it. This was my first conference paper and I’m proud to have given it at the very first session on gay studies at the Canadian national conference in English studies; the paper also became my first scholarly publication. 1 It was well received, but I knew that it would be difficult to find a job as an openly gay man working on gay topics. And it was difficult. But I’m not bitter: at about this time, queer theory emerged as a field, leading to many scholarly publications and conferences and eventually to courses and to the existence of scholars in every discipline across the humanities and social sciences who specialized in queer studies. I got a job, and then another one, and now, as I am nearing the end of my career, I look back with some pride at my small role in establishing queer studies.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.349 | 0.062 |
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".