Policing political mega-events through ‘hard’ and ‘soft’ tactics: reflections on local and organisational tensions in public order policing
Why this work is in the frame
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Bibliographic record
Abstract
Public order policing has long been a central area of concern for policing political mega-events. Following the Toronto 2010 Group of 20 (G20) meeting, however, public order policing policy and practice attracted renewed attention that has had a considerable influence on subsequent political mega-events. The Toronto G20 involved up to 20,000 protesters, over 1000 arrests, and widespread criticisms against the Toronto Police Service and Royal Canadian Mounted Police regarding excessive use of force. Using the Brisbane 2014 G20 as a case study, this article reflects on the localised tensions involved in public order policing in the context of political mega-events. We look inside the operations of Brisbane 2014, which was heavily influenced by the events from Toronto 2010, to focus on the tensions that underpin public order policing tactics in the context of political mega-events and call attention to the significance of these tensions in shaping policing policy and practice. More particularly, we examine how tensions between competing perceptions of risk amongst security actors in relation to more formal preferred strategies and tactics to manage risk can shape various public order policing outcomes. We trace these local and organisational tensions as they relate to so-called ‘hard’ tactics such as intelligence operations and spatial containment strategies and ‘soft’ tactics such as negotiated management strategies and relationship building with protest groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it