Securing the Brisbane 2014 G20 in the wake of the Toronto 2010 G20: ‘Failure-inspired’ Learning in Public Order Policing
Bibliographic record
Abstract
Extending inquiries into the dynamics underpinning the ‘iterative’ development of security governance at mega-events, this article explores practices of knowledge sharing and policy transfer at major political summits. Through detailed interviews with police involved in the Toronto 2010 G20 and the Brisbane 2014 G20 summits, and through analysing supporting documentation, we examine the ways in which police interpret past events, as either ‘failures’ or ‘successes’, specifically in the context of public order policing. The article extends insights into how such perceptions are facilitated through transnational exchanges, particularly where event-related ‘failures’ might be considered as a benchmark for iterative policy developments. We explain this process as a form of ‘failure-inspired social learning’ that questions the effectiveness, norms and legitimacy of established policies, practices and institutions involved in security governance, which can influence future transformations in global ‘best practices’.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.032 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".