Bibliographic record
Abstract
Sometimes an author manages to capture the essence of an article with such an arresting name that you feel compelled to read it. A few years ago I was scanning a list of references when I came across a title so striking that I went to a library at once, to read through the article in question and see if it lived up to its promise. It was called: ‘Hyper-Tension: a folk illness with a medical name’.1 The author of the paper was the American social anthropologist Dan Blumhagen. I was not disappointed. I now regard it as one of the most enlightening pieces of social science research that I have ever read. And although it is nearly a quarter of a century old—an aeon in terms of most academic writing—I still regularly use it as a set text when teaching groups of doctors and asking them to think about their patients’ medical ideas, and their own. Blumhagen reported on how he interviewed 117 men attending a hypertension clinic over a period of 12 months, in order to establish their beliefs about the condition. He found that each person appeared to have an individual model of what 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.
How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".