Protests as Sites of Learning: A Case Study of the Sri Lankan Tamil and Anishinaabe Demonstrations in Canada
Bibliographic record
Abstract
Social movements, in Canada and around the world, offer a rich window to study social actors as they struggle for power and justice. Relatively less explored in the academic literature is the role of social movements as a site of learning. In this article, the authors synthesize several theoretical frameworks for social movement learning and apply them to extensive fieldwork, in 2009, during Sri Lankan Tamil protests in Toronto and during Indigenous land and water defence in Beausoleil First Nation. They argue that such movements can serve as a powerful educational environment and explore how learning takes place by understanding the sources and sharing of knowledge, how the social identities of participants are consequential for learning, and the underlying social, economic, and political forces that shape movements and their emergence. Ultimately, the authors claim that understanding the learning that takes place in social movements in Canada helps us understand broader political struggles and discourse in the Canadian context and beyond, including critical new forms of solidarity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.041 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".