Asking for help: who's listening?
Bibliographic record
Abstract
CASE “I fled from the room” As a female medical student I was conducting my first physical exam on a male patient, in the presence of classmates and supervising faculty, when it became quite apparent to all that the patient had an erection. I was taken by surprise, flustered and embarrassed, and not knowing how to deal with the situation, I fled from the room. Later, my male colleagues couldn't understand my response. I know my unprofessional behavior added to the discomfort of the patient, a young man not very much older than myself. For that I am profoundly sorry. I was not prepared for such a situation and still have received no instruction as to how best to manage a similar future occurrence – for the patient's sake as well as my own. CASE “I still don't know what I did wrong” I was a third year medical student when one of our patients needed to have a nasogastric tube put in. The team turned to me and told me to see to it that this was done – and then they all disappeared. Before, in similar situations, a resident would say “Let's get the stuff and do it” but this time I found myself entirely alone. A day or two earlier, a resident had showed me the procedure; so, I went to find him. However, this time he just said “I've already shown you how to do that, just get it down. Call me if you have any problems.”
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".