Who can spot a potential problem gambler? Testing “it takes one to know one” and acquaintanceship effects in a university student population
Bibliographic record
Abstract
Abstract How easy is it for individuals to detect low to moderate levels of problem gambling tendencies in others? Are individuals who have problem gambling tendencies themselves, or are close relationship partners, more accurate judges? We examine these questions in two studies involving a total of 336 interacting dyads drawn largely from a university student population. In Study 1 all pairs were strangers, whereas in Study 2 approximately half of the pairs were close. After the “judge” observed the “target” complete a gambling task, the dyad had a face‐to‐face discussion, with topics including favorite pastimes and personal weaknesses. Judges estimated the target's problem gambling tendencies, and both judges and targets self‐reported their own gambling tendencies. There was evidence of modest, albeit somewhat inconsistent, accuracy in individuals’ judgments of the other person's problem gambling tendencies, but no “it takes one to know one” or acquaintanceship effects were apparent. Results also indicated that judges evidenced a projection bias, whereby they saw the target as similar to themselves, especially within close pairs. These results reveal that even after minimal interaction with a stranger individuals can be able to judge the person's gambling tendencies with some accuracy. At the same time, our findings indicating that close others and those with problem gambling tendencies themselves are not more or less tuned in to the early signs of a problem than anyone else suggest that it would be inappropriate to be especially convinced by—or skeptical of—these individuals’ judgments.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".