The International Partner as Invited Guest: Beyond Colonial and Import–Export Models of Medical Education
Bibliographic record
Abstract
The dominant model of international collaboration in medical education, both currently and in the past two centuries, is one of foreign (i.e., Euro-American) ownership and control. In this Invited Commentary, the authors provide a brief selected history of such international partnerships. They then focus on recent partnership models that have alternative structures. One of these is the collaborative partnership between Addis Ababa University (AAU) and the University of Toronto. This partnership is known as the Toronto Addis Ababa Academic Collaboration (TAAAC). From the inception of this partnership, the TAAAC has aimed to be relational and has firmly placed ownership of the codeveloped curriculum at AAU. Other explicit aims of the TAAAC are to help AAU develop culturally appropriate programming that is sustainable with local resources and to develop capacity-building, coteaching models. In seeking potential precedents to the TAAAC, the authors have explored archives in Ethiopia, Canada, and the United Kingdom. They found that invited foreign guests have played a role in the development of educational systems in Ethiopia since the 1940s. The authors believe that by paying close attention to the language used to describe the nature of a relationship, medical educators may be able to move toward more collaborative, capacity-building international partnerships.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".