Everyone is victimized or only the naïve? The conflicting discourses surrounding identity theft victimization
Bibliographic record
Abstract
Identity theft impacts millions of North Americans annually and has increased over the last decade. Victims of identity theft can face various consequences, including losses of time and money, as well as emotional, physical, and relational effects. Scholars have found that institutional messaging surrounding identity theft places responsibility on individuals for their own protection, which can mask institutions’ roles in identity theft’s prevalence. This paper presents findings from interviews with Canadian victims of identity theft and argues that conflicting discourses surround this crime. While identity theft victimizations are viewed as inevitable in the digital age, victims are often simultaneously stereotyped as old, naïve, or non-technologically savvy. Within this context, this research also finds that victims can express varying degrees of self-blame for having provided perpetrators with information or for having not better protected themselves. Finally, this paper argues that victims’ embarrassment and self-blame may impede help-seeking and reporting.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".