Probing the past: historical case study analysis to inform more just and sustainable global health partnerships in education
Bibliographic record
Abstract
INTRODUCTION: Disparities of power between high-income (HICs) and low- and middle-income countries (LMICs) have long characterised the structures of global health, including knowledge production and training. Historical case study analysis is an often-overlooked tool to improve our understanding of how to mitigate inequalities. METHODS: Drawing from the contemporary experience of collaborators from Canada and Ethiopia, we chose to examine the historical relationship between Ethiopian Emperor Haile Selassie and Canadian Jesuit Lucien Matte as a case study for international collaborations based on the model of an 'invited guest'. We used critical historical context and qualitative content analysis methodologies to assess written correspondence between them from the 1940s to the 1970s and drew from postcolonial theory to situate this case study in a broader context. RESULTS: The respectful and responsive relationship that developed between Emperor Haile Selassie and Lucien Matte reveals important characteristics needed for meaningful collaborations in global health education. Matte came to Ethiopia fully cognizant of the imperial context of his work and prepared to take on the position of invited guest. As a result, many of both Matte and Haile Selassie's goals were achieved. At the same time, however, this case study also revealed how problematic constructions of authoritative power can arise even when productive partnerships among individuals occur. Matte and Haile Selassie's collaboration reinscribed belief in the superiority of western theories of intellectual and social development. In addition, their prescriptive vision for education in Ethiopia repeatedly dismissed competing local positions. CONCLUSION: As international partnerships in global health education continue to exist and form, historical case studies offer valuable insights to guide such work. Among the most crucial arenas of knowledge is the need to understand powerful dynamics that have and continue to shape HIC-LMIC interaction. The historical case study of Matte and Haile Selassie reveals how problematic power differentials can be reinforced or mitigated.
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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 | 0.040 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".