Bibliographic record
Abstract
I don't remember exactly how I learned to be a “good patient.” I can't remember which was more powerful—the examples of my mother's behaviour and that of others in the waiting room or hospital, or the instructions to “Be good, to not complain about pain, to not take too much of these important people's time.” I do remember learning clearly that part of being “good” at the doctor's was to say whatever he or she wanted to hear. At the doctor's, it wasn't lying—it was making a good impression, and that was what mattered. It mattered especially at the hospital—you had to make sure they liked you, so you got better care and didn't wait so long. We were a family of “lower socioeconomic status” and “non-English speaking background,” and the most important thing was to nod and say “Yes, doctor” no matter how mystified you were—and no matter how far-fetched the advice was in terms of our “social context.” “Getting plenty of rest?” “Yes, doctor.” I was a child in the 1960s. In the 1980s, when I became a health consumer advocate, at first I jumped into the active, rights driven, approach so clearly described by several writers in this issue. But there was always …
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.028 | 0.041 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".