Plural policing, the public good, and the constitutional state: an international comparison of Austria and Canada – Ontario
Bibliographic record
Abstract
For the past two or three decades many jurisdictions have experienced a pluralisation of policing. In addition to the regular public police, in most countries new providers have become involved in policing public and semi-public places. This paper deals with the differences in the ways that plural policing and its consequences are defined and assessed in different countries. The paper focuses on two countries that differ considerably in the impact of neoliberalism, Austria and Canada – Ontario. In these countries different discourses are used to assess plural policing and its potential negative impact. In Canada the public good is the central concept in discussions of plural policing. This often refers to instrumental goals such as value for money and service delivery to consumers. In Austria plural policing is generally discussed in terms of the tasks and position of the state, the monopoly of violence, and by referring to constitutional and fundamental legal perspectives. This study shows that international comparative research on (plural) policing cannot be based on the tacit assumption that central concepts such as public good have universal relevance. On the contrary, these normative concepts are highly context dependent, an important conclusion for future international comparative research on policing and security.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".