Bibliographic record
Abstract
Abstract As trans people become more visible, form stronger communities, and continue to find new platforms to tell trans stories, our conversations surrounding sex and gender expand, become more nuanced, and we are able to articulate new ways of understanding and embodying gender. This article begins with a discussion of three autobiographical texts by trans-identified men: Jamison Green’s Becoming a Visible Man (2004), Matt Kailey’s Just Add Hormones (2005) and Max Wolf Valerio’s The Testosterone Files (2006). I argue that these texts all belong to a ‘second wave’ of trans activism and advocacy that was emerging in the 1970s and 1980s, around the time that the authors began socially and physically transitioning. This ‘second wave’ of trans activists moved away from dominant heteronormative narratives of trans identity of the ‘first wave’ that relied on respectability politics, but continued to build on the idea of a clear, cohesive and enduring sense of gender identity. I then move on to interrogate the idea of a ‘third wave’ of trans discourse through an analysis of two blogs maintained on Tumblr.com by authors who originally identified as binary trans men, but whose experiences with moments of ‘unintelligibility’ in social and discursive contexts lead them to question the idea of a clear, cohesive and enduring sense of gender identity.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".