Bibliographic record
Abstract
As a child, I remember watching intently my pediatrician's steel stethoscope as it swung back and forth on his neck, like a hypnotist's pendulum, lulling me into a near-panicky dread of its cold metallic shock on my skin. Then, just before he placed it on my chest — I would brace myself, every single time — he would miraculously remember to warm it up with his hands. And all fear would be forgotten. Nowadays, technology has “progressed”: we do not have those cold stethoscopes any more. Instead, we have an armamentarium of much colder and darker things, like MRI machines, bronchoscopes and MRSA masks. Modern textbooks talk about things like blood samples, CT scans and MRIs as being “more dependable than the physical exam,” but that's not the point. These tests are the idioms of a modern medical jargon that patients simply do not speak. Their language is the language of the physical exam, however pointless it may seem to us at times. In a strange metaphorical way, I feel that it is now my duty to warm up the stethoscope, somehow, through explanation and shared concern, to lessen the cold shock of the unnatural devices and procedures we now use to help our patients. The first step in achieving this is to understand that our notion of what constitutes caring for the patient does not necessarily (and probably does not usually) coincide with the patient's idea of what it is to be cared for. Recently, I went to see a specialist for a recurrent problem that I have had for as long as I can remember. Roughly, our interaction went as follows: after we introduced ourselves to one another, I candidly told him exactly what the problem was, detailing it as any self-respecting medical student would. He acknowledged the problem and proceeded to ask me exactly how I would like things to be: essentially, what I thought he could do for me. After this, he took a moment to consider the problem, comb through the details and cut to the heart of the matter. He posed a few more questions and pondered further. Next, he offered his expert opinion and treatment plan and asked if I understood and agreed with his strategy. Finally, he proceeded with an extensive examination and the first treatment. Before I knew it, conversation was flowing freely, taking root in the frivolous banalities of small talk and blooming — an hour later — into the sharing of views and goals and, indeed, the sharing of many personal stories, as between friends. The power differential between the expert and his subject, and the disempowering act of sharing a personal concern with a stranger and putting myself “in his hands” seemed much easier now that the expert was also a person. Before I left that day, I scheduled another haircut in six weeks and wondered, “Why can't doctors be like that?”
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.471 | 0.203 |
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".