Bibliographic record
Abstract
In many countries, COVID-19 has amplified the health, economic and social inequities that motivate group-based collective action. We draw upon the SIRDE/IDEAS model of social change to explore how the pandemic might have affected complex reactions to social injustices. We argue that the virus elicits widespread negative emotions which are spread contagiously through social media due to increased social isolation caused by shelter-in-place directives. When an incident occurs which highlights systemic injustices, the prevailing negative emotional climate intensifies anger at these injustices as well as other emotions, which motivates participation in protest actions despite the obvious risk. We discuss how the pandemic might shape both normative and non-normative protests, including radical violent and destructive collective actions. We also discuss how separatism is being encouraged in some countries due to a lack of effective national leadership and speculate that this is partially the result of different patterns of social identification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.007 |
| 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".