Society for the Study of Addiction Annual Conference 2024
Bibliographic record
Abstract
funded by sources with a vested interest in the outcomes of that research introduces a risk of funding-related bias.This risk has been an issue of concern in multiple domains, including alcohol, tobacco and medical research.One area in which the issue has been a topic of much debate is the study of gambling-related harms; however, there is currently no evidence-based method of identifying research that may have a high or low risk of funding-related bias.This project aims to develop a reliable, valid instrument to estimate the risk of funding-related bias in gambling studies.To do so, the following activities are being undertaken: (1) conducting a rapid search of the literature to review past work in related fields; (2) conducting a modified e-Delphi study with international researchers and funders to identify factors that contribute to risk of funding-related bias and their weights; and (3) developing and validating an instrument to assess risk of funding-related bias.Further details on the study can be found in the protocol on Open Science Framework: https://osf.io/vncp5/.This tool will be able to support several outcomes, including outlining information for inclusion in funding calls and disclosure statements and improving understanding of the impact of funding source on research area and design and the subsequent potential for influence on policy and legislation.Disclosures M.M. Young is Greo Evidence Insights (Greo)* liaison with the Academic Forum for the Study Gambling (AFSG)** and was employed for 12 years by the Canadian Centre on Substance Use and Addiction, which received funding from the Government of Canada.S. Stark is the Director of Research and Evidence Services at Greo Evidence Insights (Greo)*.Prior to 2022, S. Stark was employed at the Responsible Gambling Council, where, in the past 5 years, she worked on projects funded by the Alcohol and Gaming Commission of
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.242 | 0.093 |
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".