The “power few” of missing persons’ cases
Notice bibliographique
Résumé
Purpose The purpose of this paper is to test the “power few” concept in relation to missing persons and the locations from which they are reported missing. Design/methodology/approach Data on missing persons’ cases ( n = 26,835) were extracted from the record management system of a municipal Canadian police service and used to create data sets of all of the reports associated with select repeat missing adults ( n = 1943) and repeat missing youth ( n = 6,576). From these sources, the five locations from which repeat missing adults and youth were most commonly reported missing were identified (“power few” locations). The overall frequency of reports generated by these locations was then assessed by examining all reports of both missing and repeat missing cases, and demographic and incident factors were also examined. Findings This study uncovers ten addresses (five for adults; five for youths) in the City from which this data was derived that account for 45 percent of all adults and 52 percent of all youth missing person reports. Even more striking, the study data suggest that targeting these top five locations for adults and youths could reduce the volume of repeat missing cases by 71 percent for adults and 68.6 percent for youths. In relation to the demographic characteristics of the study’s sample of adults and youths who repeatedly go missing, the authors find that female youth are two-thirds more likely to go missing than male youth. Additionally, the authors find that Aboriginal adults and youths are disproportionately represented among the repeat missing. Concerning the incident factors related to going missing repeatedly, the authors find that the repeat rate for going missing is 63.2 percent and that both adults and youths go missing 3–10 times on average. Practical implications The study results suggest that, just as crime concentrates in particular spaces among specific offenders, repeat missing cases also concentrate in particular spaces and among particular people. In thinking about repeat missing persons, the present research offers support for viewing these concerns as a behavior setting issue – that is, as a combination of demographic factors of individuals, as well as factors associated with particular types of places. Targeting “power few” locations for prevention efforts, as well as those most at risk within these spaces, may yield positive results. Originality/value Very little research has been conducted on missing persons and, more specifically, on how to more effectively target police initiatives to reduce case volumes. Further, this is the first paper to successfully apply the concept of the “power few” to missing persons’ cases.
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 | 0,001 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».