Embedded Addiction: The Social Production of Gambling Knowledge and the Development of Gambling Markets
Bibliographic record
Abstract
This paper discusses features of the development of gambling markets, particularly the production of gambling knowledges that interact with and contribute to the constitution of these markets. While gambling expansion is predicated on the provision of “entertainment,” and where gambling is marketed as the consumption of safe risks, there is also the possibility of risky consumption and the production of problem gamblers. The paper discusses the knowledge produced around the figure of the problem gambler, and how the figure has been productive in terms of institutional developments. The risks posed by the problematic gambler (e.g. to state-owned gambling enterprises) must be “solved” in order to enable the legitimization of markets and the ongoing quest for profits/revenues. The paper analyzes knowledge production by situating this production in relation to contemporary sociological orientations to consumption and risk in late modernity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".