Negotiation, Mediation, Globalization Protests and Police: Right Processes; Wrong System, Issues, Parties and Time
Bibliographic record
Abstract
In dealing with anti.globalization protesters, police forces in Canada and the United States are experimenting with a new proactive strategy of negotiation and mediation. This strategy addresses two key objectives: protecting the safety of police, protesters, meeting delegates and the public, and facilitating lawful dissent. It has been credited, most recently at the 2002 G8 Summit in Alberta, with reducing violence on the part of protesters. However, in the author's view, an emphasis on dialogue between police and protesters raises several concerns. First, it tends to involve protestors within the political system that they are trying to change from the outside, thus limiting their ability to bring about such change. Second, focusing the dialogue on issues of safety and the acceptable level of dissent draws attention away from the fundamental issues that concern the protesters, such as fair trade, workers' rights, the environment and debt relief Third, the dialogue is with the police, rather than with the government and corporate representatives whom the protesters are trying to reach. Fourth, at this point in the history of the anti·globalization movement, police-protester negotiations may dampen the dissent and debate that me needed to bring real institutional change.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".