Bibliographic record
Abstract
I continue my exploration of gaming law by examining casino design. I will argue that in Canada – where gaming is under the rigid monopolistic control of the Provincial Governments – that casino design may be as much of a legal question as it is a psychological and architectural issue. When it comes to casino design, there are two models that ‘dominate’ the landscape. The model proposed by David Krane argues that casinos should be designed like ‘adult playgrounds. In this model, the comfort and ‘homely’ feeling of a casino is emphasized, as is open spaces, high ceilings, water and vegetation. In contrast, the Friedman International Standards of Casino Design focus on player counts and utilitarian features. In this model, the design and layout of the gaming machines and tables is central. Low ceilings, congested gaming equipment, short sight lines and narrow, winding aisles (that lead to isolated, intimate playing areas) are key. Ontario casinos are said to be a blend of the Krane and Friedman design principles. In this article, I explore the viability of a Canadian casino that is designed largely on the Friedman Design Principles, and suggest that many of these “international standards” or “winning principles” may not apply in Canada. And unlike George Costanza from Seinfeld, I feign no expertise in architecture (although I did have a passing interest in marine biology at one time).
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.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".