Is There A Relationship between Participation in Gambling Activities and Participation in High-Risk Stock Trading?
Bibliographic record
Abstract
The purpose of the present study was to investigate whether or not there is an association between engaging in traditional forms of gambling and engaging in high-risk stock trading and, if so, to examine game play patterns of high-risk stock traders, as well as identify any socio-demographic similarities or differences between the two groups. Logistic regressions on data from two large Canadian data sets were undertaken to examine which variables best differentiate traditional gamblers from high-risk stock traders. The results indicate that high-risk stock traders have a higher frequency of gambling, engage in a larger range of gambling activities, and are more likely to be problem gamblers. Additionally, the type of gambling activities that high-risk stock traders participate in suggests that they are a sub-group of skill-based gamblers who also prefer gambling on casino table games, sports betting, dog and horse race betting, and games of skill for money over chance based games such as electronic gaming machines, bingo, and instant win tickets. High-risk stock traders, compared to traditional gamblers were more likely to be male, have a higher income, be better educated, and to be of Asian or “other” descent, not be divorced, widowed or separated, and be self-employed or employed full-time. However, unlike other skill-based gamblers, high-risk stock traders tended to be older rather than younger, and had a high income rather than a low income.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".