Bibliographic record
Abstract
CASE “The commander” When I was a fourth year medical student beginning a surgical rotation, I was assigned to the operating room of a chief whose reputation had earned him the name “The commander.” At a certain point in the procedure, the resident was called away leaving the intern and several medical students. The surgeon singled me out and ordered me to suture the patient. I protested, explaining this was my second day and I did not feel qualified. He yelled back that “the only way to learn is to plunge in.” As I hesitated he became enraged, slapped my shoulder and pushed me toward the table. I obeyed, despite serious misgivings, but when I made a minor mistake he struck my hand sharply with an instrument. For some time after I suffered with a bruised shoulder and a cut finger. COMMENTARY Law schools instruct their students that any un-consented-to touching constitutes a technical battery. Any threatening gesture, even when interrupted, which was meant to produce fear in the targeted party in torts law amounts to an assault, giving rise to a potential recovery even for what is effectively an incomplete battery.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".