How Luhmann’s systems theory can inform gambling studies
Bibliographic record
Abstract
Gambling and problem gambling studies tend to be characterised by individual-based approaches both theoretically and methodologically, while sociological approaches remain underutilised or even marginal. In this study, we discuss the potential of Niklas Luhmann’s systems theory in the analysis of gambling. As opposed to positivist or individualistic approaches, Luhmann’s work is strongly constructivist: neither systems nor their components are seen to be made up of individuals. Using systems theory in informing gambling research distances the research interests from individuals and directs it towards societal mechanisms, structures, and processes. Therefore, a systems theoretical approach can offer novel tools to study gambling, but also the paradigm of gambling research itself. This paper demonstrates how systems theory can critically inform gambling research through five operationalisations: gambling as a system, the gambling experience, the regulation of gambling economies, gambling providers as organisations, and systems theory as a methodological program. These five operationalisations can serve as an important window to widen perspectives on gambling.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".