U.S. and Canadian Higher Education Protests and University and Police Responses, 2012-2018
Bibliographic record
Abstract
The authors describe protest patterns at U.S. and Canadian universities in the 2010s. The research draws on a new dataset, the Higher Ed Protest Event Dataset, which combines machine learning and sociological hand coding of 16,069 campus newspaper articles. The sample consists of 5,553 higher ed protests involving 584 universities and colleges between 2012 and 2018. The dataset also includes university and police responses to a subset of protests. The authors find that protest frequency is patterned by the academic calendar. The top issue in both U.S. and Canadian higher education protests was university administration and governance. The comparative analysis reveals distinctive patterns in other issues raised and protest intensity. In the United States, the periods of greatest protest activity were waves of mass mobilization across the country on often racialized issues with a national dimension: racist police violence, racially hostile campus climates, and Donald Trump’s presidency. In Canada, protest activity was most intense during provincial or local campaigns led by formal student organizations and unions on issues of economic security: public tuition, austerity, and labor conditions. Across both countries, university administrations and police usually avoided extensive intervention during protests. The findings contribute to social movements research through methodological innovations and new empirical insights on movements in higher education.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".