Notice bibliographique
Résumé
As we’ve observed with numerous policing initiatives over the years, programs can quickly become derailed as a result of a lack of forethought to the issue of long-term sustainability (Willis et al, 2007; Bradley and Nixon, 2009; Kalyal et al, 2018). Of all the topics we cover in this book, this is likely the one that is easiest to describe in theory but that will generate the most difficulty in practice. The reason for this is simple: the adoption of evidence-based policing (EBP) requires not only a degree of investment in individuals as change agents, but also, for some agencies, what might appear to be a radical rethinking of how police services engage in decision-making. While we recognize that the exigencies of policing can sometimes be best met by the traditional command and control structure, an evidencebased organization is one that embodies a learning culture – that is, an institutional culture that places emphasis on seeing operational and administrative decisions as opportunities for implementing innovation and learning lessons from both success and failure. It is also one that requires a more decentralized approach to viewing expertise and soliciting input and feedback into decision-making. In this chapter, we draw on the relevant management and other literatures, as well as on our own and others’ experiences, to make some specific and some broader recommendations for how to generate a sustainable EBP approach within small organizations. Practical solutions Investing in internal resources A study was recently published showing that police officers experience increased job satisfaction when engaged in problemsolving activities within their communities (Sytsma and Piza, 2018). This should hardly be surprising. When organizations hire intelligent, thoughtful, analytical people with diverse knowledge and skills, those same people want to do work that is both intellectually and emotionally satisfying. Or, as one of us recently posted, ‘let smart people do smart work.’ The reality, however, can be very different. Another study, this one on crime analysts, revealed that very few of those surveyed were involved in program evaluation or other forms of experimentation (Piza and Feng, 2017). As the authors suggest – and we agree – this is a tremendous waste of internal resources (Piza and Feng, 2017), and can lead to some analysts feeling their work is little more than ‘wallpaper’ (Innes et al, 2005: 52).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,400 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».