Bibliographic record
Abstract
Over the past 15 years, provincial governments across Canada have consistently demonstrated their willingness to expand the availability of gambling (Campbell et al. 2010). Most recently, the Ontario Lottery and Gaming Corporation (OLG) unveiled expansion plans that included eliminating 17 slots-at-race track venues and replacing them with 29 casinos nested more closely to popula-tion centres. The singular driving force for expansion is govern-ment’s quest for non-tax revenue, largely in response to an ideologically based disaffection for tax increases. The trade-off is that, without precedent, government becomes directly involved in providing an activity that knowingly harms the population it is elected to serve. This fact alone demands unique policy consideration in relation to how government implements and manages its gambling agenda (Smith and Rubenstein 2009). Using the current Ontario initiative as a case in point, this article explores several of these considerations. First Principles
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".