A thematic analysis identifying concepts of problem gambling agency: With preliminary exploration of discourses in selected industry and research documents
Bibliographic record
Abstract
The focus of this exploratory analysis was the idea and locus of agency in conceptualisations of gambling and problem/pathological gambling within corporate and academic domains as presented in public discourses. In order to unpick and analyse how such agency is being conceptualised and presented, the author carried out a preliminary thematic analysis of selected public documents. While annual financial reports, academic articles, and public testimony constituted the sample for analysis, the intention was to propose a methodology and framework of analysis that might be applied by future researchers to an expanded selection of documents deemed to be of interest. A notable overlap of themes was found wherein agency for (problematic) gambling was placed with individual gamblers against an assumed neutral backdrop of free-market forces, with industries only agentic in responding to the consumption demands of freely choosing (and implicitly self-actualising) individuals (except where credit is taken for the generation of increased consumption as translated into profits). In conclusion, it is suggested that the legitimacy and practice of political-economic and institutional analyses be reclaimed, providing complementarity to current reflections on the nature of agency and assisting us to better understand the notion of (gambling-related) harm production.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".