What Might We Be Saying to Potential Applicants to Medical School? Discourses of Excellence, Equity, and Diversity on the Web Sites of Canada’s 17 Medical Schools
Bibliographic record
Abstract
PURPOSE: Medical school Web sites often advance arguments to claim institutional excellence and appeal to the "best and the brightest" who might join their institutions as medical students. What do these texts communicate about institutional excellence, or the excellence of potential applicants to medical school? How are discourses related to social accountability, such as those concerning diversity and equity, represented? METHOD: From July through December 2010, using the concepts of excellence, equity, and diversity, the authors examined the discourses identified on the Web sites of Canada's 17 medical schools, focusing on faculty welcome pages, deans' messages, and those pages specifically targeting applicants to medicine. RESULTS: Institutional prestige and applicant suitability were generally promoted through discourses of academic excellence such as research, innovation, and global positioning. Service-to-society discourses were much less prominent. Diversity discourses emerged primarily as appeals to institutions' cosmopolitan sophistication. Equity, when mentioned, tended to focus on increasing the participation of indigenous and rural students in medicine. Institutional positioning can be situated on a continuum from the more "centric" (typical academic excellence claims) to the more "eccentric" (excellence claims grounded in local contexts such as service to a region or constituency). CONCLUSIONS: Discourses can play a central role in regulating social institutional practices. It is worthwhile for medical schools to examine the messages that medical schools are communicating on their Web sites. If schools are to move beyond prestige-based characterizations of excellence and build a socially accountable profession, open and inclusive discussions are needed.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".