Identity Theft Victims’ Understandings of Incidents and their Reporting Decisions
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".