Bibliographic record
Abstract
Public order policing, in particular the policing of protest, is increasingly complex, ambivalent and unpredictable. Contemporary large-scale protests, often an amalgam of diverse and diffuse affinity groups and agendas, represent considerable challenges for police. As regulatory agents, police encounter the perennial dilemma of how to reconcile the rights of protesters to express their grievance while maintaining order, safety and security. Adapting to Protest , the report of Chief Inspector Denis O’Connor of Her Majesty’s Inspectorate of Constabulary (HMIC, 2009, p. 5) into the G20 London protests on 1 April 2009, highlights this public order paradox: ‘Balancing the rights of protesters and other citizens with the duty to protect people and property from the threat of harm or injury defines the policing dilemma in relation to public protest.’ William Blair, Toronto Police Service Chief, after the 18–24 June 2010 G20 summit, asserted that ‘the policing challenges of facilitating these very large, lawful, peaceful protests, while at the same time, arresting those who chose violence and destruction were immense’ (Toronto Police Service, 2011, p. 3). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".