Beyond the cultural myth of medical meritocracy
Bibliographic record
Abstract
BACKGROUND: We examine the cultural myth of the medical meritocracy, whereby the "best and the brightest" are admitted and promoted within the profession. We explore how this narrative guides medical practice in ways that may no longer be adequate in the contexts of practice today. METHODS: Narrative analysis of medical students' and physicians' stories. RESULTS: Hierarchies of privilege within medicine are linked to meritocracy and the trope of the "hero's story" in literature. Gender and other forms of difference are generally excluded from narratives of excellence, which suggests operative mechanisms that may be contributory to observed differences in attainment. We discuss how the notion of diversity is formulated in medicine as a "problem" to be accommodated within merit, and posit that medical practice today requires a reformulation of the notion of merit in medicine, valorising a diversity of life experience and skills, rather than "retrofitting" diversity concerns as problems to be accommodated within current constructs of merit. CONCLUSIONS: Three main action-oriented outcomes for a better formulation of merit relevant to medical practice today are suggested: (a) development of assessors' critical consciousness regarding the structural issues in merit assignment; (b) alignment of merit criteria with relevant societal outcomes, and (c) developing inclusive leadership to accommodate the greater diversity of excellence needed in today's context of medical practice. A reformulation of the stories through which medical practitioners and educators communicate and validate aspects of medical practice will be required in order for the profession to continue to have relevance to the diverse societies it serves.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".