How Luhmann’s systems theory can inform gambling studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it