Underlying cognitions in the selection of lottery tickets
Bibliographic record
Abstract
There is evidence that the faulty cognitions underlying an individual's playing behavior maintains and supports their gambling behavior. Sixty undergraduate students completed the South Oaks Gambling Screen (SOGS), a measure to assess pathological gambling, and a questionnaire ascertaining the type and frequency of their gambling activities. Sixteen Loto 6/49 tickets were presented to participants and ranked according to their perceived likelihood of being the winning ticket. The numbers on the tickets were categorized as: long sequences (e.g., 1-2-3-4-5-6), patterns and series in a pseudo-psychological order (e.g., 16-21-26-31-36-41), unbalanced (e.g., six numbers from 1-24 or 25-49), and those appearing to be random (e.g., 11-14-20-29-37-43). Verbal protocols of ticket selections were ranked into eight heuristics. Results revealed that for the entire sample the greatest percentage of tickets chosen for the first four selections were "random" tickets. Further, the most commonly cited reason for selecting and changing a lottery ticket was perceived randomness. The results are discussed with reference to the cognitions used when purchasing lottery tickets.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".