Bibliographic record
Abstract
I'm sorry I haven't written to you in a while; springtime came and before I could turn around the trees were green again, the sun high and hot, and it was August.I wonder if time knows its own flow or if it's just as surprised to be here as we are.I got a chance to watch that TV show you recommended -the one with the Autistic doctor.You know I don't really watch that much TV.I usually just watch the same old movie for those first five minutes over and over again.I can barely sit still for a full episode, but I tried really hard this time because people kept recommending it to me.Because you kept asking me to.It still feels novel to see an Autistic character on TV.I remember when I first started educating, nobody had heard of autism, now we have our own caricatures on prime-time television.I must admit, most of the books on autism I read when I was younger were written by doctors or people with letters after their names.Strange to read about myself in a cold, pathological way -to see my life broken down into case studies and diagrams and clinical-speak.I think after a while you get used to hearing your story spun out as pathology; your pieces broken down into "behaviors," "skills to work on," and "deficits."After a while I left the textbooks alone.There's only so much you can take, David.Only so many hours you can sit with a book and see the parts your parents highlighted as they found you somewhere in between the pages of medical-speak.I left them and found the books that parents wrote.Their journeys "through" autism, their "struggling" to "overcome" their child's diagnosis, them "reclaiming" their child, all with rave reviews from other non-Autistic writers about how brave and honest they were to talk about their child's life in such intimate detail -my life and all its intimate details -and what their child's diagnosis meant for them.I left those books behind after a
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.010 |
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".