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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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 teacher head, 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".