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Enregistrement W2731484330

Missing Persons and Social Exclusion

2013· dissertation· en· W2731484330 sur OpenAlexaboutno aff
Laura van Dongen

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2013
Typedissertation
Langueen
DomainePsychology
ThématiqueGrief, Bereavement, and Mental Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSocial exclusionPsychologyData sciencePolitical scienceComputer scienceLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

People who go missing are often perceived to have done so voluntarily, and yet, many missing persons in Canada are Aboriginal, visible minorities, homeless, and are fleeing from violence, abuse, and neglect. Integrating the concept of social exclusion and an intersectional perspective with a sample of 724 missing persons cases drawn from one Canadian police service, this dissertation examines the systemic issues underlying peoples’ disappearances. This dissertation also explores the role of social and economic disadvantage in the risk of a long term disappearance. A combination of univariate (descriptions), bivariate (cross-tabulations), and multivariate (logistic regression) analyses identify correlates and causes of going missing and correlates and causes of long term disappearances. \nThe concept of social exclusion explains how structural processes prevent particular groups and individuals from gaining access to valued social relationships and economic opportunities in a particular society, resulting in considerable hardship and disadvantage. This dissertation argues that people who are marginalized and excluded have few resources to rely on to cope with stress and strain and may resort to going missing if confronted with adversity. Groups who are overrepresented among missing persons compared to the general population are identified by cross-tabulations and chi-square tests. Multivariate analysis (partial tables and logistic regression) is used to control for possible sources of spuriousness, in order to have more confidence in imputing causal relationships between membership in disadvantaged groups and going missing. \nMoreover, if disadvantaged groups go missing, they further sever ties with families, the labour market, and other mainstream institutions. As a result of extreme disadvantage, they may find it difficult to (re)connect with conventional social relationships and mainstream society. For example, youth who are escaping violence and abuse at home often end up on the streets and sever ties with schools, families, and other conventional support networks and become engaged in street culture. As a result of extreme disadvantage these young people are at risk of a long term disappearance. In other words, social exclusion is expected to be a risk and causal factor in long term disappearances. Groups who are overrepresented among long term disappearances compared to short term disappearances are identified by cross-tabulations and chi-square tests. Logistic regression analysis is used to draw conclusions about causal factors in long term disappearances. \nThis research finds that excluded groups such as disadvantaged youth, Aboriginal people, women and other visible minorities, victims of violence, and youth in care are at disproportionate risk of going missing. Consistent with an intersectional perspective, this dissertation shows that certain groups who are multiply marginalized such as Aboriginal women and young women face an especially high risk of going missing. Aboriginal identity, labour force status, and homelessness are also implicated as causal factors in peoples’ disappearances. Moreover, this research finds that social exclusion is a risk and causal factor in long term disappearances as Aboriginal people, homeless people, minorities and other excluded groups face a high risk of a long term disappearance. Linking missing persons with the concept of social exclusion highlights the role of structural issues in peoples’ disappearances and refutes the common misperception that going missing is a choice. In terms of policy, the findings from this research indicate that prevention and intervention depend on targeting poverty, discrimination, gender inequality, violence, and other structural issues associated with social exclusion.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,024
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,085
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,024
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0030,002
Études des sciences et des technologies0,0110,011
Communication savante0,0030,003
Science ouverte0,0010,010
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,272
Écart entre enseignants0,251 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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