The relationship between gambling fallacies and problem gambling.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The cognitive model of problem gambling posits that erroneous gambling-related fallacies are key in the development and maintenance of problem gambling. However, this contention is based on cross-sectional rather than longitudinal associations between these constructs, and gambling fallacy instruments that may have inflated this associated by their inclusion of problem gambling symptomatology. The current research re-evaluates the relationship between problem gambling and gambling-specific erroneous cognitions in a 5-year longitudinal study of gambling using a psychometrically sound measure of erroneous gambling-related cognitions. The sample used in this study (n = 4,121) was recruited from the general population in Ontario, Canada, and the retention rate over 5 years was exceptionally high (93.9%). The total sample was similar, in age and gender distributions, to the census data at the time of data collection for Canadian adults (18-24 years, n = 265, 55.8% female; 25-44 years, n = 1,667, 56.4% female; 45-64 years, n = 1,731, 55.4% female; 65 + years, n = 458, 44.75% female). Results of both cross-sectional and longitudinal analyses confirm that gambling-specific fallacies appear to be etiologically related to the subsequent appearance of problem gambling, but to a weaker degree than previously presumed, and in a bidirectional manner. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it