Bibliographic record
Abstract
In their new groundbreaking study reviewed in this special issue, The Trans Generation: How Trans Kids (and Their Parents) are Creating a Gender Revolution (2018), sociologist Ann Travers details the experiences of transgender children in the US and Canada, some as young as four years of age, who participated in research interviews over a five-year period. Establishing a unique picture of what it means to grow up as a trans child, Travers offers numerous examples of daily life and challenges for children like, for example, Martine and Esme, both of whom sought to determine their own gender at an early age: Martine and her family recount how at the age of seven she responded to her upcoming appointment at a gender clinic by asking if the doctor would have “the machine where you walk in as a boy and walk out as a girl,” while Esme’s story begins in preschool and leads to the care of a “trans-affirmative doctor” (168) from the age of six and the promise of hormone blockers and estrogen at the onset of puberty. Although Travers’s work is devoted to and advocates for trans children as a whole, its implications for our understanding of and research into girls and girlhood cannot be understated. What does it mean to “walk out” of that machine in the doctor’s office “as a girl?” What happens when you displace the seemingly monumental onset of puberty from its previous biological imperatives and reproductive futures? How might feminist work on girlhoods, which has sought to challenge sexual and gender binaries for so long, approach an encounter with what Travers calls “binary-conforming” or “binary-identifying” (169) trans girls or with the transgender boys in their study who, at first, respond to the conforming pressures of adolescence very similarly to cisgender girls who will not ultimately transition away from a female identity?
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.422 | 0.251 |
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".