The Gift of Policing: Understanding Image and Reciprocity
Bibliographic record
Abstract
The Community Based Policing model has been adopted by the large majority of policing agencies as another tool on an officer’s duty belt that allows them to do their job more effectively and efficiently. \nThe model is premised on the building and maintaining of relationships of the Police Service and the community it serves. The model argues that Services must ensure that the community is given a voice in the way police enforce the laws. The model encourages that the police and community work together in a partnership that is different from the traditional relationship shared between the two groups under the previous Professional Policing model. This working in partnership means that not only must the police become more open to the community providing direction in the way they do their job, but also that the community must take a more active role in the policing of their areas. This partnership could be considered an exchange of information from both the police and the community. \nAs argued by Marcel Mauss in The Gift, relationships that are on-going and have elements of exchange have obligations. These obligations of giving, receiving and reciprocity ensure that the relationship between the groups is not only maintained, but strengthened. When one of these obligations is not met, however, there are often social consequences. \nThis research attempts to understand the model of Community Based Policing in terms of how it is being applied by Canada’s second oldest police service, the Hamilton Police. With the model encouraging a relationship with the community, issues of gift exchange appear. Through interviews with staff of the Hamilton Police Service, as well as citizens from the community of Hamilton, how these obligations are being met, as well as the effectiveness of the model and its relation to Maussian theory of gift exchange are explored.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".