In Reply to van Zanten et al
Bibliographic record
Abstract
We thank van Zanten and colleagues for their interest in our review. Both the Educational Commission for Foreign Medical Graduates (ECFMG) and the World Federation for Medical Education have important influence over medical education on a global scale. We agree that for global accreditation to work, there must be a form of standardized process for approving the local accreditors and ultimately, the medical schools. Our core argument, however, is that this always carries an inherent danger of silencing important nuances in local needs and resources that affect training models. 1 We hope we have raised awareness about assumptions that are inherent in concepts, such as “standardization.” 2 Standardization is a common theme in both medicine and medical education, which may in part be driven by the biomedical paradigm of evidence-based medicine. Proponents of standardization in global standard development emphasize its role in improving quality in both training and patient care and facilitating movement of health professionals. However, as stated by Timmermans, “standardization may seem to be politically neutral on the surface, but in fact, it poses sharp questions for democracy.” 3 Standardization has been criticized for driving a loss of identity and social power and being vulnerable to implementation gaps. 3 Sefton describes the tensions inherent in meeting specific local community health needs while addressing international requirements or standards. 4 Our position is not to eliminate global efforts, such as those in accreditation and curriculum design, but to recognize the complexity and subtleties between local priorities and global differences. We acknowledge the articulated benefits of the new system proposed by the ECFMG. However, we argue that the unanticipated and unintended effects of power relations must be consciously examined, reported, and corrected. As stated in the ECFMG’s 2010 announcement, “the efficacy of such a requirement depends on a universally accepted accreditation process.” 6 Achieving a “universally accepted” accreditation process is likely impossible. For this reason, we argue that we must track which institutions/regions do not end up participating in the new system and, most importantly, why they do not. 5
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 | 0.014 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.036 | 0.038 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".