Bibliographic record
Abstract
The establishment of three provincially-owned commercial casinos in Ontario beginning in 1993 provides the empirical base for this governmentality-inspired investigation into the casinos' governing rationale. Their general notions of benefit and cost, I show, are implicitly understood within an emergent political context of profitability that economically and socially justifies casino provision. Techniques of image management assist in constructing a publicly palatable image of profit but are also reinforced by the cost-benefit framework and its subsequent spawning of an array of sub-regimes of governance and their constituent technologies. This thesis analyzes control structures that aim at minimizing the primary costs to profit, namely revenue, crime and addiction. It seeks to anatomize and describe the various technologies, power relations, languages and objectives that constitute and enable the governance of different domains deemed risky to casino profitability. Moreover, the implications for theorizing governance and specifically, risk governance are explored Lastly, a detailed analysis is performed on the programmes concerned with gambling addiction and responsibilization and their role in governing gamblers. Through these concepts, casino gambling acquires a greater social and political significance. As I argue, they are ultimately projects that perpetuate and produce a subjectivity and behavioural set that reflects appropriate citizenship for contemporary society and all its conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".