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Record W4405045115 · doi:10.1111/add.16729

Why it is important to conduct gambling research that is fair and free from conflicts of interest

2024· letter· en· W4405045115 on OpenAlexafffundabout
Amanda Roberts, Jim Rogers, Steve Sharman, Sasha Stark, Simon Dymond, Elliot A. Ludvig, Richard J. Tunney, Matt O’Reilly, Matthew M. Young

Bibliographic record

VenueAddiction · 2024
Typeletter
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCarleton UniversityCanadian Centre on Substance Use and AddictionGreo
FundersGambleAwareGambling Research Exchange OntarioLeverhulme TrustNational Institute for Health and Care ResearchOntario Ministry of Health and Long-Term CareUK Research and InnovationHealth and Care Research Wales
KeywordsHarmHarm reductionGovernment (linguistics)Conflict of interestPublic relationsPsychologyPerspective (graphical)CriminologyPolitical scienceSocial psychologyPublic healthLawMedicine

Abstract

fetched live from OpenAlex

This a letter to the editor of Addiction to assert the importance of gambling research that is both free from bias and informed by a diversity of perspectives, including those who have direct experience of gambling-related harm.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.267
metaresearch head score (Gemma)0.640
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.966
Threshold uncertainty score0.903

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2670.640
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0060.040
Scholarly communication0.0190.029
Open science0.0060.008
Research integrity0.0340.030
Insufficient payload (model declined to judge)0.0150.009

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 head0.508
GPT teacher head0.479
Teacher spread0.030 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainIncentives
GenreCommentary

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

Citations2
Published2024
Admission routes3
Has abstractyes

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