Donna becomes Don: A Call for school districts to better serve transgender youth
Bibliographic record
Abstract
For as long as she could remember, Donna did not want to be a girl. This inner conflict existed throughout her childhood development. Donna resides in a rural area and excels in sports, many of which are male dominated, such as mountain biking, skiing, snowboarding, and dirt biking. Self-identified as a tomboy; everyone assumed she was a boy. Donna becomes Don is the story of a courageous young person who begins the journey of transitioning while still in elementary school (age 12, grade 7) within a conservative school district that has next to no resources. The intent of this case is to stretch the thinking of those teachers and administrators who are uncomfortable or who have not worked with transgender youth before, in hopes that they will be more prepared and more open when the occasion presents itself. Don’s story serves to illuminate the changing needs of students on the margins. Teachers and school administrators need to gain awareness, develop skills, and professional qualities, which will better serve the youth for whom they are caring.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".