ETHICS CASE Pay for Performance: What We Measure Matters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it