Crimping the Croupier: Electronic and mechanical automation of table, community and novelty games in Australia
Bibliographic record
Abstract
Technological innovation has increased electronic and mechanical automation to traditional games that replace or augment human croupiers, and also change how the games are enjoyed. Little is known about how these automated products may influence people's gambling or entice new players to try these table and community games. Research regarding the characteristics of electronic gaming machines (EGMs) has provided insights into the potential consequences associated with technological enhancements. However, without knowing how these products differ to their traditional counterparts, it is difficult to begin to understand their implications on player expenditures and product safety. An Australian national environmental scan of these electronically and mechanically enhanced table-game and community-game products was conducted to identify the characteristics of these automated products Australia-wide. Based on EGM research (Armstrong & Rockloff, 2015), the "VICES" framework was identified as an appropriate organising principle for surveying the features of automated products. The VICES acronym specifies 5 criteria by which automated products might differ from traditional table-games: (v)isual and auditory enhancements, (i)llusion of control, (c)ognitive complexity, (e)xpedited play, and (s)ocial customisation. The findings suggest that automation provides the potential for the provision of products that intensify gambling engagement with the attendant potential for gambling-related harm. Further research, however, is needed to find if this potential harm is manifest in real-world gambling environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".