Review of Whelan and Molnar’s Securing Mega-Events: Networks, Strategies, and Tensions
Bibliographic record
Abstract
Public order policing has never been more controversial than it is today.Mega-events are set-piece occasions for large-scale public-order policing.Occasions such as the Olympics, Commonwealth Games, and Pan-American Games as well as the geo-political summits of the G7, G8, or G20 are all examples of megaevents.These are planned security events, and police plan them well in advance.Documenting and describing these security operations offers lessons in police institutional thinking that are relevant beyond the specifics of the case studies in this book.This book gives readers an inside view of policing at two mega-events: one in Toronto, Canada, in 2010 and the other in Brisbane, Australia, in 2014, with the latter described in considerable detail.It provides a comprehensive account of the police co-operation networks that are the organizational foundation for both planning such events and managing the police operations intended to secure them.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".