Bibliographic record
Abstract
Controversy has surrounded the activity of gambling ever since its legalization under the authority of the provincial governments in Canada. Due to the nature of the activity, there are several delicate and complicated issues that arise. On both sides of the debate, there are numerous arguments in favour of and against legalized gambling. The government argues that legalized gambling is a way of creating new jobs and earning revenue without raising taxes. On the other hand, there are those who argue that the political integrity of the governments comes into question. In addition, there are several hidden social costs that result from legalized gambling. Issues such as problem gambling, crime, and unemployment seem almost inevitable. When analyzing the question of legalized gambling it is important to calculate the costs and benefits that come with it. Research so far has shown that there seems to be more of a cost rather than a benefit with legalized gambling. The social toll that gambling creates is far greater than the monetary benefits that are generated. In fact, in the long run, the monetary benefits may just be negated because of the actions that need to be taken to deal with the social costs. Therefore, this paper shall argue that legalized gambling is a detriment to society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".