Freedom from discrimination or freedom to discriminate? Discursive tensions within discrimination policies in medical education
Bibliographic record
Abstract
The importance of advancing equity, diversity, and inclusion for all members of the academic medical community has gained recent attention. Academic medical organizations have attempted to increase broader representation while seeking structural reforms consistent with the goal of enhancing equity and reducing disproportionality. However, efforts remain constrained while minority groups continue to experience discrimination. In this study, the authors sought to identify and understand the discursive effects of discrimination policies within medical education. The authors assembled an archive of 22 texts consisting of publicly available discrimination and harassment policy documents in 13 Canadian medical schools that were active as of November 2019. Each text was analysed to identify themes, rhetorical strategies, problematization, and power relations. Policies described truth statements that appear to idealize equity, yet there were discourses related to professionalism and neutrality that were in tension with these ideals. There was also tension between organizations’ framing of a shared responsibility for addressing discrimination and individual responsibility on complainants. Lastly, there were also competing discourses on promoting freedom from discrimination and the concept of academic freedom. Overall, findings reveal several areas of tension that shape how discrimination is addressed in policy versus practice. Existing discourses regarding self-protection and academic freedom suggest equity cannot be advanced through policy discourse alone and more substantive structural transformation may be necessary. Existing approaches may be inadequate to address discrimination unless academic medical organizations interrogate the source of these discursive tensions and consider asymmetries of power.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".