The Economic “Impact” of a Downtown Casino in Toronto
Bibliographic record
Abstract
This report will not add to the already overly abundant collection of completely meaningless numbers that are being thrown around and at the citizens of Toronto and Ontario. Rather, this report will ask questions — most of which have gone unanswered and unaddressed so far during this process. If the city of Toronto decides it wants to allow a casino in the downtown core of the city and on or dominating a significant place on the limited resource that is its waterfront, the city should be well-aware to what it is saying “yes”. The appendix provides a review of the peer-reviewed academic literature that has been published on the regional economic impact of casinos. That literature forms the basis for this report. It should be carefully noted that this report only focused on the economic impact. The social, moral, individual, and family impacts of casinos and legalized gambling are separate, but important, issues that should also be considered. However, this report only focuses on the potential regional (Toronto, GTA) impacts of a downtown casino. This report will focus on three areas: Jobs, Neighbourhood, and City.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".