Bibliographic record
Abstract
Conventionally, medicine deals with those troubles that we agree to define as diseases. However, the selection of diseases from the range of problems that afflict us is neither inevitable nor straightforward, as illustrated by the much debated candidate cases of those alphabet disorders of modernity such as RSI (repetitive strain injury) and ME (myalgic encephalopathy). Given the large body of work that explores the emergence, construction, and negotiation of diseases, Duffin perhaps makes heavy weather of convincing readers that diseases are not merely biological categories, but emerge when social demand and medical possibility coalesce.coalesce. Figure 1 Jacalyn Duffin University of Toronto Press, £29.95/$27.50, pp 229 ISBN 0 8020 3805 0 Rating: ★★⋆⋆ Her first case study is lovesickness. Drawing on a wide range of classical literature and medical writing, Duffin describes how the dysfunctions of love have been, since classical times, not only metaphorically considered as illness, but at times also literally medicalised as disease. Does lovesickness really exist? Duffin is ambivalent on the status of underlying biological realities. Some “symptoms” seem stable over the centuries, she suggests, but not its credibility as a medical problem. She draws on phenomena as diverse as adultery, nymphomania, venereal disease, sex manuals, and masturbation to argue that “love was once a card-carrying disease” (p 65) but appeared to disappear in the 20th century. However, overtones of disease persist in concepts such as transference, crimes of passion, co-dependency, and brain scans suggesting that love is similar to obsessive compulsive disorder. These lists are fascinating in their passing details, but raise the question about the legitimacy of tracing such equivalences through time. How can we know that there is a real underlying illness if we recognise it only from its endlessly varied manifestations, sometimes medicalised, sometimes not? How can we read historical writings on love from anything other than a 21st century understanding of what that means? Duffin's arguments suffer a real tension between the relativism of a historian recognising that biology has been a rather different object through the centuries, and the fastidiousness of a clinician anxious to correct a few wrong assumptions on the way, such as the “gender bias” in the management of heart disease. More generally, though, from what privileged vantage point can we assess what is a bias, responsible for incorrectly framing a disease concept, and what are the social forces that create diseases? The story of the emergence of hepatitis C is one of litigation and cultural mores about deserving and undeserving sufferers. Political needs had their part in shaping medical research that constructed a new disease from what was essentially a left over category of liver disorder, and dividing it into two diseases with different meanings depending on how it was contracted, through blood transfusion, or through lifestyle. But Duffin has already implied that it could not be otherwise: we cannot have a pure disease, untainted by the unpleasantness of politics and morality, for illnesses cannot become diseases without a social network to make them possible. Both stories end with a plea for a more population based approach to disease, in which problems (whether they are those of women reluctant to leave violent husbands, or injecting drug users at risk of hepatitis) are seen as residing in the social order, rather than within a medical model. Again, hardly a new idea, but one well worth reiterating. Not all troubles are, or should be, the province of medicine.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.012 |
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".