Bibliographic record
Abstract
Reviewed by: Terminal Damage: The Politics of VLTs in Atlantic Canada W.D. Walls Terminal Damage: The Politics of VLTs in Atlantic Canada by Peter McKenna. Fernwood Publishing, 2008. In this well-organized book, McKenna does a superb job of identifying and explaining issues of public policy surrounding the video lottery terminal (VLT) gambling business in Atlantic Canada. Drawing on numerous sources, McKenna illustrates the coincidence of interests that has led to a proliferation of the VLT industry and the human side-effects associated with it. The author states this on the back cover with such clarity and precision that my words are no substitute for his: The truth is that financial stress, marriage break-up, physical and mental health issues, depression, alcohol dependency and suicide go hand-in-hand with pathological gambling. In spite of this obvious scourge, governments knowingly turn a blind eye toward the social fallout. What they do have an eye for is the annual revenue figures derived from gambling. VLTs, by far the most addictive of all gambling activities, are also the biggest cash cow. This book examines why public policy action has been more symbolic than [End Page 384] substantive and how VLTs have become increasingly politicized since the early 1990s. It is a clarion call to governments to take real action. An overview of the institutional and historical development of gambling in Canada—and VLTs in particular—is provided in the first chapter. This chapter provides not only a brief legal and legislative background on gambling in Canada, but also the gain of control over lotteries and VLTs by the provinces based on a model adopted by various US states. For each of the Atlantic provinces, McKenna systematically by chapter documents the introduction of VLTs, their effects, and the political landscape. The mechanics of the political process surrounding VLT gambling is thoroughly documented; while some readers seeking analytical or empirical evidence might occasionally get bogged down in the detailed descriptions of the lives of addicts, McKenna succeeds in passionately putting a human face on the social problems associated with VLT gambling. One of the pillars of VLT proliferation is that the same provincial governments that benefit financially from gaming revenues — including the important component accounted for by VLTs — are their own regulators. As McKenna puts it, “There is a manifest conflict of interest here, in the sense that governments are seeking to benefit financially from a sector of the economy in which they have regulatory authority” (p. 15). A second pillar of VLT proliferation is the crown corporation in charge of gaming. “As an organization with a staff, budget resources, and a public policy mandate, its business priorities are to ensure its survival as a bureaucratic entity, to expand its operational reach if possible, and to fortify its reason for being” (p. 17). Together with local businesses that benefit from VLT revenues, McKenna demonstrates an iron triangle of interests that has led to the expansion of VLT gambling. As long as governments are able to control or limit any political fallout, they are likely to continue to cash in on the potential for increased gaming revenues. Our knowledge of the costs and benefits of gambling markets is incomplete. The cost side is particularly difficult to quantify since many of the costs are intangibles resulting from compulsive gambling behaviour. In this work there is no attempt to quantify the costs of gambling, nor is there any quantification of expenditures on counselling, public relations campaigns, or studies of the gambling sector. The human cost is illustrated in the tales of woe of VLT addicts. Governments have implemented so-called responsible gaming strategies to combat the social cost of the gaming industry. In reality, though, governments are doing precious little of any real substance in confronting the growing problem of video lottery gambling: it is all about smoke and mirrors when it comes to VLTs. We are repeatedly told that they will do everything humanly possible to deal with the VLT mess they themselves have created—short of any real, substantive action, of course, (p. 208) Because this book does not bring systematic data collection or statistical analysis to bear on the policy issues at hand...
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".