Toronto Addis Ababa Academic Collaboration: A Relational, Partnership Model for Building Educational Capacity Between a High- and Low-Income University
Bibliographic record
Abstract
Educational partnerships between academic health sciences centers in high- and low-resource settings are often formed as attempts to address health care disparities. In this Perspective, the authors describe the Toronto Addis Ababa Academic Collaboration (TAAAC), an educational partnership between the University of Toronto and Addis Ababa University. The TAAAC model was designed to help address an urgent need for increased university faculty to teach in the massive expansion of universities in Ethiopia. As TAAAC has developed and expanded, faculty at both institutions have recognized that the need to understand contextual factors and to have clarity about funding, ownership, expertise, and control are essential elements of these types of collaborative initiatives. In describing the TAAAC model, the authors aim to contribute to wider conversations and deeper theoretical understandings about these issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".