Foucault's "fearless speech" and the transformation and mentoring of medical students
Bibliographic record
Abstract
In his six 1983 lectures published under the title, Fearless Speech (2001), Michel Foucault developed the theme of free speech and its relation to frankness, truth-telling, criticism, and duty. Derived from the ancient Greek word parrhesia, Foucault's analysis of free speech is relevant to the mentoring of medical students. This is especially true given the educational and social need to transform future physicians into able citizens who practice a fearless freedom of expression on behalf of their patients, the public, the medical profession, and themselves in the public and political arena. In this paper, we argue that Foucault's understanding of free speech, or parrhesia, should be read as an ethical response to the American Medical Association's recent educational effort, Initiative to Transform Medical Education (ITME): Recommendations for change in the system of medical education (2007). In this document, the American Medical Association identifies gaps in medical education, emphasizing the need to enhance health system safety and quality, to improve education in training institutions, and to address the inadequacy of physician preparedness in new content areas. These gaps, and their relationship to the ITME goal of promoting excellence in patient care by implementing reform in the US system of medical education, call for a serious consideration and use of Foucault's parrhesia in the way that medical students are trained and mentored.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.107 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.001 | 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".