The conceptual and empirical relationship between gambling, investing, and speculation
Bibliographic record
Abstract
Background and aims To review the conceptual and empirical relationship between gambling, investing, and speculation. Methods An analysis of the attributes differentiating these constructs as well as identification of all articles speaking to their empirical relationship. Results Gambling differs from investment on many different attributes and should be seen as conceptually distinct. On the other hand, speculation is conceptually intermediate between gambling and investment, with a few of its attributes being investment-like, some of its attributes being gambling-like, and several of its attributes being neither clearly gambling or investment-like. Empirically, gamblers, investors, and speculators have similar cognitive, motivational, and personality attributes, with this relationship being particularly strong for gambling and speculation. Population levels of gambling activity also tend to be correlated with population level of financial speculation. At an individual level, speculation has a particularly strong empirical relationship to gambling, as speculators appear to be heavily involved in traditional forms of gambling and problematic speculation is strongly correlated with problematic gambling. Discussion and conclusions Investment is distinct from gambling, but speculation and gambling have conceptual overlap and a strong empirical relationship. It is recommended that financial speculation be routinely included when assessing gambling involvement, and there needs to be greater recognition and study of financial speculation as both a contributor to problem gambling as well as an additional form of behavioral addiction in its own right.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".