'They're Very Lonely': Understanding the Fraud Victimisation of Seniors
Bibliographic record
Abstract
There are many theories which seek to explain fraud victimisation. In particular, older victims find themselves at the intersection of various discourses which account for victimisation, primarily from a deficit model. This article examines two discourses relevant to older fraud victims. The first positions older victims of crime as weak and vulnerable and the second positions fraud victims generally as greedy and gullible. Using interviews with twenty-one Canadian volunteers who provide telephone support to older fraud victims (all seniors themselves), this article analyses the extent to which these two discourses are evident in the understandings of these volunteers. It finds that volunteers overwhelmingly perceive fraud to occur out of loneliness and isolation of the victim, and actively resist victim blaming narratives towards these individuals. While neither discourse is overly positive, the article discusses the implications of these discourses for the victims themselves and for their ability to access 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.029 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".