Bibliographic record
Abstract
The dawn of the 21st century has witnessed the unfolding of a new season of grassroots political activism across the world. Heralded by the mass-demonstrations that took place in 1999 in Seattle—where anti-globalization activists successfully blocked the negotiations of the World Trade Organization, physically preventing delegates from accessing the site of the meeting—in the last decade an intense wave of transnational protests has shaken the main insti-tutions of global governance, from the G8 to the IMF, from the World Bank to the G20. Thus, cities like Prague, Genoa, Gothenburg, Evian, Quebec City, London, New York, and San Francisco have become the theaters of new forms of direct action and mass-protest against corporate globalization, free-trade agreements, global poverty, environmental destruction, and (particularly since 2003) the ‘war on terror’. This resurgence of political radicalism, coupled with the strategies of public order deployed by law enforcement agencies in response to the acts of civil disobedience often performed by grassroots activists, has inspired a growing body of academic literature on the policing of dissent (see Della Porta and Reiter, 1998; Fernandez, 2008). However, most of this literature has focused on the changing paradigms of public order policing, while relatively little attention has been devoted to an understanding of the dynamics of social protest from the standpoint of the activists who engage in mass-demonstrations, direct action, and nonviolent resistance.Why do grassroots activists violate the law? What motivates nonviolent people to confront huge numbers of heavy armed police? How do otherwise law-abiding citizens manage to endure the traumatic experience of being arrested, and in some cases even imprisoned, for their acts of civil disobedience? How do these activists cope (both individually and collectively) with the ‘pains of imprisonment’? And how are their private and public lives affected by these experiences?
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.078 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".