Personal Luck Usage Scale (PLUS): psychometric validation of a measure of gambling-related belief in luck as a personal possession
Bibliographic record
Abstract
Luck is by definition a random event. However, many people believe luck to be something it is not – an internal, personal quality. An obstacle for understanding personal luck and its sequelae among gamblers has been the lack of a psychometrically sound measure. The current paper reports the development of the Personal Luck Usage Scale (PLUS). In Studies 1 and 2 (Ns = 347 and 361, respectively), a one-dimensional, eight-item scale emerged and was subsequently confirmed among university-aged gamblers. Importantly, the PLUS was distinguishable from a general belief in luck (Study 2). In Study 3 (N = 60), a behavioural consequence of belief in personal luck was assessed among a community sample of gamblers. Specifically, PLUS scores were positively associated with the average amount of money spent in a gambling session. The implications of a belief in gambling-related personal luck for the progression and maintenance of problem gambling are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".