Medical disinterestedness: an archaeology of scientificness and morality in the Canadian medical profession
Bibliographic record
Abstract
In this dissertation I consider the emergence of and the shifts in the scientific and moral standards in the Canadian medical profession, or what I call medical disinterestedness. I examine editorial content from medical journals as a discursive space in which professional norms are constituted. I draw on the works of Pierre Bourdieu in order to argue that doctors are enmeshed in a unique system of rewards that cannot be explained by an economic model based on profit. I investigate three crisis moments during which Canadian doctors faced accusations from the public, the media and the government for not acting with scientific and moral judgment. The first crisis moment I examine occurs in the nineteenth century when doctors faced a hostile government that refused them the right to govern all aspects of medicine. During this time, doctors drew on middle-class masculine codes of etiquette and their privileged access to university education in order to claim that they were learned gentlemen acting on behalf of the public. This claim was called into question during the 1950s-1960s, however, when the Canadian media shamed the medical profession for opposing the proposal for a universal health care system. In this second crisis moment, in order to restore their moral credibility, doctors upheld general practice and public health as humane forms of medicine and adopted media relations strategies aimed at improving their image. Opening the doors to the media created complications, however, as demonstrated in the third crisis moment when the editors at the top Canadian medical journal were fired in 2006. This event revealed that the supposedly pure intellectual space of medical science collides with media-market forces, professional politics and journalism in ways that have troubling ramifications for medical practice. My analysis of these crisis moments demonstrates that morality, objectivity and ethics are not fixed concepts but are rather shaped in relation to historical, social, cultural, political and economic factors. This dissertation extends ethical discussions in medicine to include the ways in which doctors define and communicate what it means to act with integrity in relation to the state, professional politics and the media.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.032 | 0.026 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".