Contrary to Psychological and Popular Opinion, There Is No Compelling Evidence That Older Adults Are Disproportionately Victimized by Consumer Fraud
Bibliographic record
Abstract
According to psychological and popular opinion, older persons are especially susceptible to consumer fraud. Research on cognitive and affective aging reveals age-related changes that could increase the vulnerability of older persons to consumer fraud. However, this research does not show that consumer fraud actually is more prevalent among older persons. In generalizing from laboratory findings of cognitive decline to age differences in the prevalence of consumer fraud, psychologists may underestimate the influence in everyday life of possible protective factors associated with old age, including increased experience and changes in goals, lifestyle, income, as well as purchasing and risk behaviors. We review evidence on the prevalence of consumer fraud and conclude that there is no clear indication that it is more prevalent among older persons. Aggregating across all consumer frauds, there is evidence that consumer fraud is less common among older persons than adults of other ages. This evidence is potentially flawed, however, because of failings inherent in the methodologies. In the absence of irrefutable data, it is premature to conclude that consumer fraud is less prevalent among older adults, but it is also premature to conclude that consumer fraud is more prevalent among older persons, as is assumed in conventional and psychological wisdom.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".