Protest in unlikely times: dynamics of collective mobilization in Europe during the COVID-19 crisis
Bibliographic record
Abstract
Based on an original protest event analysis (PEA) dataset covering 30 European countries, this paper provides three sets of results. Despite its unlikeliness due to lockdowns and social distancing measures, protest during COVID-19 has hardly been put to a halt even if, as a result of the restrictions imposed by the lockdown measures on the opportunities of public collective actions, protest occurred at significantly lower levels compared to pre-COVID-19 times, in terms of number of events and, above all, in terms of the number of participants. Moreover, protest was refocused on COVID-19-related issues, in particular on protest against the restrictions imposed by the government lockdowns, while non-COVID-19 issues, in particular economic issues, were crowded out. In addition, protest during the COVID-19-crisis also responded to highly contingent national context conditions which varied between the different regions of Europe.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".