Keeping Chance in Its Place: The Socio-Legal Regulation of Gambling
Bibliographic record
Abstract
In the winter of 2010, driving through a blizzard to a research interview outside of Ottawa, one of the co-editors of this special issue—Kate Bedford—slid and spun off the road in her rental car. The interviewee—an 80-year-old man who organized a small weekly bingo game—helped dig her out. Sitting in the community centre with him afterwards, thawing, there was ample opportunity for Bedford to reflect on the diverse meanings attached to gambling and the complex ways in which it is regulated. The interviewee talked about ‘use of proceeds’ forms and validating expenses payments for volunteers, describing a gambling landscape that seemed a long way from dominant law and policy conversations. While commentators on the global financial crisis were drawing repeated analogies to casinos and poker, the less glamourous world of small-town bingo seemed to have slipped from view. This special issue is, in part, an effort to bring it back. In 2013, inspired by research in Ontario, Bedford began work on a large, international research grant into gambling regulation. Rather than focusing on relatively well-researched forms of gambling, such as casinos, the project centred bingo as a distinctively under-studied gambling sector. The second co-editor, Donal Casey, joined the initiative in 2015, believing that online gambling could provide a crucial new lens for his research into European Union (“EU”) law and regulation. As part of the research project, Bedford, Casey, and others convened a conference at the University of Kent in 2016 on socio-legal approaches to gambling, where scholars from nine countries and a number of disciplines presented their research. The seven papers that we have collected in this special issue are drawn from that conference, including one from our third co-editor, Alexandra Flynn. In this Introduction to the collection, we lay out what these papers offer to the field of gambling research and beyond. To begin, we identify the scholarly approaches to gambling upon which we wish to build (Part I). Then, we specify three contributions we seek to make through our socio-legal endeavors. First, this collection seeks to foreground the diverse, vernacular forms and places of play that are sometimes overlooked in gambling scholarship (Part II). Second, the papers take a distinctive pluralist approach that recognizes the multi-layered character of gambling regulation (Part III). Third, and finally, the interdisciplinary and methodologically-diverse nature of this special issue allows the papers, alongside the contributions in the Voices and Perspectives section, to speak to a wide range of debates within and outside academia (Part IV).
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 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 | 0.000 | 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 | 0.000 |
| 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".