Identity Theft Victims’ Understandings of Incidents and their Reporting Decisions
Notice bibliographique
Résumé
Identity theft, the theft and misuse of another person’s identity information, has increased in North America over the past decade, with almost 10 percent of adults victimized annually. Although few victims pay out of pocket for identity theft incidents, other consequences can include lost time and emotional, relational, and physical tolls. Despite these costs to millions of North Americans annually, most incidents are reported to financial institutions rather than police. In Canada, no representative identity theft victim surveys have been administered since its criminalization, and little is known about the scope and nature of identity theft. Two recent studies have examined identity theft reporting in the United States, but few studies have explored victims’ experiences qualitatively. This study employs a symbolic interactionist perspective to better understand victims’ perceptions of identity theft and their reporting decisions. Drawing from interviews with 20 identity theft victims in Ontario and quantitative analysis of the United States’ 2016 National Crime Victimization Survey – Identity Theft Supplement, it finds that victims’ understandings of the incidents they faced are processes and that identity theft is conceptualized in multiple ways: including as crimes, routine inconveniences, institutional failures, and relational issues. Many participants resisted identification as a victim for various reasons, including that they felt they experienced little harm or that they held some blame for the incident. In terms of reporting, quantitative analyses revealed that more serious incidents were reported to law enforcement and that the reporting of victims who paid out of pocket was impacted differently by other measures of seriousness compared to those who recuperated losses. Interview participants often took the actions they saw as practical based on their understanding of the incident, while their reporting decisions were also influenced by emotional reactions and advice. Finally, participants’ reporting experiences varied significantly: while some had their needs met promptly, others had to call institutions repeatedly or were referred elsewhere. This study makes important contributions to the existing research on identity theft victimization, and future research should continue examining this understudied area since the results suggest that many victims may fall through the cracks of victim support.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,006 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».