Policing political mega-events through ‘hard’ and ‘soft’ tactics: reflections on local and organisational tensions in public order policing
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.070 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".