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Record W4415634992 · doi:10.29173/cgs215

Reframing gambling harms as the product of a predatory industry

2025· article· en· W4415634992 on OpenAlexvenueno aff
Thomas Mills, C Jenkins, James W. Grimes, James P. Sampson, Paula Reavey, Susie Sykes

Bibliographic record

VenueCritical Gambling Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersLondon South Bank UniversityUniversity of WestminsterNational Institute for Health and Care Research
KeywordsCognitive reframingFraming (construction)NarrativePublic healthRationalityProduct (mathematics)Context (archaeology)

Abstract

fetched live from OpenAlex

The framing of public health challenges influences how societies and governments respond to them. This paper suggests that public health professionals counter the claims and influence of harmful community industries by amplifying the reframing efforts of progressive social movements. We engage with Jurgen Habermas’ Critical Theory, which lends theoretical support for this argument, while a practical example is presented of a network which sought to shift gambling harms narratives to focus on harmful industry products and practices. Habermasean constructs inform an analysis of 33 semi-structured interviews, including people with Lived Experience (LE) of gambling harms. Habermas’ ideas, notably his diagnosis of modern social problems as antagonism between the System and the Lifeworld, provide political-economic context to the emergence of a LE social movement. Habermas’ notion of communicative rationality underpins both the internal logic of this movement and public health professionals’ attempt to nurture a ‘counterpublic’ around it: i.e., a space for new ways of thinking and talking about social issues. Paradoxically, the findings reveal the importance and limitations of local collaborations with people affected by harmful industries in the face of those industries’ power, products and advertisements. The findings have implications for the theory and practice of commercial determinants research.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.189
GPT teacher head0.506
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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