Bibliographic record
Abstract
The development of the Internet has kindled many new business opportunities in the online environment. Despite the recent slump in online business growth and popularity, one line of online business is generating profit and growing at a rapid rate: the business of online gaming.\nThe legality of such businesses is questionable in Canada and there are few gaming cases to assist Canadian lawyers. The following analysis must be considered in light of the dearth of jurisprudence in this area and should not be considered legal advice. This area of the law is in flux and developments may be unpredictable.\nWhen you are feeling in the dark, even a flickering candle is welcome. Therefore, a recent British Columbia online gaming prosecution, in which the accused pled guilty, is worthy of study. Though resolved by a guilty plea with little judicial reasoning, the case provides some guidance in this largely unmapped area. It confirms that in certain circumstances, there can be criminal liability in Canada for running an online gaming operation. Online gaming ventures will have to consider several factors and be mindful of how their ventures are structured in order to avoid prosecution in Canada and conform to Canadian laws.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.036 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".