ONLINE SECURITY BEHAVIORS PREDICT SCAM DETECTION ABILITY IN OLDER AND YOUNGER ADULTS
Bibliographic record
Abstract
Older adults are the fastest growing group of technology adopters (Anderson & Perrin, 2017). While this can open doors to many exciting opportunities, it also comes with security concerns. According to the Federal Trade Commission, internet scams are now more common than those that take place through any other type of modality (Anderson, 2013). Fraudsters can reach thousands of targets with very little effort or cost (Symantec, 2017). The internet offers a unique scamming context as deception cues like tone of voice and facial expressions are lost. Therefore, it is important to understand how people of all ages interact with potential scam emails. 160 healthy older (60–90 years of age) and younger adults (18–30 years of age) completed a scam detection task, during which they were asked to identify emails as either legitimate or fraudulent. They also answered questions about their technology behaviors outside of the lab and completed cognitive testing (Heaton et al., 2014). For both younger and older adults, internet security behavior (i.e. installing a pop-up blocker, using secure passwords) was the strongest predictor of scam detection ability, above and beyond demographic and cognitive factors (p < .001). The findings suggest that not only are there differences in the ability to accurately identify an email scam, but those who are worse at this skill may also be putting themselves at risk in other online contexts. Multi-context approaches to online safety behavior may be particularly helpful for fraud prevention programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".