Tra lo striscione e il manganello: Gli agenti mediatori all'interno delle manifestazioni
Bibliographic record
Abstract
For the province of Quebec, the year 2012 marked a fundamental rupture within the traditional management of public order. The conventional organisation of Montreal police forces has been challenged by the emergence of a mass contestation movement, the “Printemps Érable”, that held around 700 manifestations in the space of 7 months. During these protests, the figure of the “liaison officer” emerged from the structure of Montreal Police Service (S.P.V.M.). Acting as mediators between the police and the protesters, their role in the management of public demonstrations will be analysed in this paper, through a series of semi-structured interviews done with the mediators team. Drawing from this data, several issues will be discussed. Firstly, the efficiency of this recent police practice and mediator agents own perceptions on the functioning of this organizational structure in similar contexts. Secondly, the delicate position of the mediator agent, who stands “in between” policemen (involved in policing the public order with a traditional approach) and protesters. Third, the fact that mediator police officers main challenges are to obtain recognition of their key role and to gain the confidence of the other two groups of social actors. Finally, we will also emphasize how mediator officers construct a singular depiction of the protester through their discursive 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".