Learning to listen to trans and gender diverse children: A Response to Zucker (2018) and Steensma and Cohen-Kettenis (2018)
Bibliographic record
Abstract
The authors answer recent responses by Steensma & Cohen-Kettenis (2018 Steensma, T. D. , & Cohen-Kettenis, P. T. (2018). A critical commentary on “A critical commentary on follow-up studies and “desistence” theories about transgender and gender non-conforming children”. International Journal of Transgenderism . Advance online publication. doi:https://doi.org/10.1080/15532739.2018.1468292 [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Zucker (2018 Zucker, K. (2018). The myth of persistence: Response to AA critical commentary on follow-up studies and “Desistance” theories about transgender and gender non-conforming children. International Journal of Transgenderism . Advance online publication. doi:https://doi.org/10.1080/15532739.2018.1468293 [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) to our critical commentary on “desistance” stereotypes and their underlying research on trans and gender diverse children (Temple Newhook et al., 2018 Temple Newhook, J. , Pyne, J. , Winters, K. , Feder, S. , Holmes, C. , Tosh, J. , … Pickett, S. (2018). A critical commentary on follow-up studies and “desistance” theories about transgender and gender-nonconforming children. International Journal of Transgenderism . Advance online publication. doi:https://doi.org/10.1080/15532739.2018.1456390 [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). We provide clarification in the following areas: (1) the scope of our paper; (2) our support of longitudinal studies; (3) consequences of harm to trans and gender diverse children; (4) clinical practice implications; (5) concerns about validity of research methodology; and (6) the importance of learning to listen to trans and gender diverse children.
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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 | 0.032 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.010 | 0.027 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.052 | 0.088 |
| Insufficient payload (model declined to judge) | 0.005 | 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".