Canadian Universities and International Development: Learning from Experience
Bibliographic record
Abstract
This introduction locates the special issue's focus on Canadian universities and development within the current discourse on “internationalization.” We argue that the push for the internationalization of universities does not necessarily address international development unless universities demonstrate a strong commitment to make development, and its related goals of poverty reduction, social justice, and global citizenship, central to their teaching, research, and outreach functions. Our own project experiences in Brazil and Vietnam are used as background to discuss the insights emerging from the papers that follow. The paper concludes that universities could simultaneously enhance their contributions to development, while strengthening themselves as learning institutions. They could structure more lasting partnerships with developing country institutions, approach projects and partnerships in a spirit of mutual learning through engagement with complex social problems rather than as knowledge transfer exercises, develop more collaborative relationships with funding agencies, better integrate development work with teaching and research missions, and apply resources to the ongoing study of universities themselves.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".