What does ‘race’ have to do with medical education research?
Bibliographic record
Abstract
CONTEXT: We live in a world of ethnoracial conflict. This is confirmed every day by opening and reading the newspaper. This everyday world seems far away in the pages of a medical education journal, but is it? The goal of this paper is to suggest that one need not look very far in medical education to encounter ethnoracial issues, and further, that research methods that are not ethnoracially biased must be employed to study these topics. DISCUSSION: We will draw attention to the relevance of employing an ethical conceptual approach to research involving 'race' by demonstrating how one author researching internationally educated health professionals has put 'race' front and centre in his analysis. He does this by using a postcolonial method of analysis termed a 'doubled-research' technique that sets up categories such as 'race' but then decolonizes them to avoid essentialism or stereotyping. We compare this method to another mainstream method employed for the same topic of inquiry which has sidelined 'race' in the analysis, potentially hiding findings about ethnoracial relations involving health professionals in our 'multicultural' society. This demonstration leads to the important question of whether research methods can be epistemologically racist-a question that has been raised about conventional research on education in general. Our argument is not meant to be the last word on this topic, but the first in this journal. CONCLUSIONS: We conclude that there is an internal ethics or axiology within research perspectives and methodologies that needs to be examined where ethnoracial issues are prominent. The use of mainstream approaches to undertake research can unintentionally 'leave unsaid' central aspects of what is researched while antiracist methods such as the one described in this article can open up the data to allow for a richer and deeper understanding of the problem.
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 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 | 0.303 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.022 | 0.113 |
| Scholarly communication | 0.028 | 0.048 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".