Correlates of responding to and becoming victimized by fraud: Examining risk factors by scam type
Bibliographic record
Abstract
Abstract Consumer fraud reports in North America have been increasing each year along with median fraud losses. Using survey data from 1375 American and Canadian consumers who previously reported a scam to a North American consumer complaint organization, this study examines the correlates of responding to and losing money to four categories of consumer fraud: opportunity‐based scams, threat‐based scams, consumer purchase scams, and phishing scams. Relative to opportunity‐based scams that offer the promise of rewards, consumers were less likely to respond to and report losing money when solicited by threat‐based scams and phishing scams. The odds of victimization were highest for consumer purchase scams. Risk factors, including gender, race, education, income, loneliness, financial fragility, and financial literacy, differed across scam categories, suggesting that victim profiles differ across fraud types. Some of the risk factors associated with responding to the scam solicitation (vs. ignoring it outright) were different from risk factors associated with victimization. Having advance knowledge of fraud prior to being exposed was protective across nearly all scam types. Results suggest that awareness about specific scams helps protect against financial loss. Additional research is needed on how to effectively deliver fraud awareness messages to those who are most susceptible.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".