ETHICS CASE Pay for Performance: What We Measure Matters
Bibliographic record
Abstract
Mr. Ozonoff arrived at Dr. Mehta’s office for his annual checkup. His blood pressure had been in the normal range until a few months ago, when it had started to creep up, according to the blood pressure machine he sometimes used outside his workplace cafeteria. At Dr. Mehta’s office, it registered 145/90—just into the hypertensive range. Dr. Mehta wanted to get Mr. Ozonoff’s blood pressure back into the normal range and thought the goal could be achieved by changes in his eating and exercising habits. At the same time she recognized that her practice received a financial bonus every quarter from several of the health plans they contracted with when a certain percentage of the patient panel maintained blood pressures within the normal range, and medication was the surest and simplest way to accomplish the goal quickly. Because Mr. Ozonoff’s blood pressure was only slightly above the 140/90 cutoff for hypertension, Dr. Mehta began to discuss lifestyle changes—such as regular exercise and eating a healthier, lower-salt diet — with him, changes that would help not only with his blood pressure but with other health problems; his weight, for example, had
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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 | 0.096 | 0.349 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 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".