The Effect of Skilled Gamblers on the Success of Less Skilled Gamblers
Bibliographic record
Abstract
This paper uses computer simulations to examine the effect of highly skilled gamblers on the success of moderately skilled gamblers. It shows that skilled players negatively impact the outcome for less skilled players. A player's winnings are not only affected by the house rake or vigorish but also by the skill of other players. It is concluded that less skilled players are often better off playing a game of chance than a game of skill. It is our contention that professionals in the field of gambling studies can gain a great deal of insight into problem gambling by closely examining the games gamblers play. The purpose of this article is to examine some differences between games that involve some skill and those that involve only chance in order to help treatment and prevention workers understand the dynamics of these games. For example, understanding the nature of the game and its effects on the individual gambler can help a therapist understand a client's motives and beliefs, which may facilitate a more individualized, client-centered approach to the treatment.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".