Integrating open science practices into recommendations for accepting gambling industry research funding
Bibliographic record
Abstract
Diverse funding sources, including the government, nonprofit, and industry sectors support academic research, generally, and gambling research, specifically. This funding allows academic researchers to assess gambling-related problems in populations, evaluate tools designed to encourage responsible gambling behaviors, and develop evidence-based recommendations for gambling-related topics. Some stakeholders have raised concern about industry-funded research. These critics argue that industry funding might influence the research process. Such concerns have led to the development of research guidelines that aim to preserve academic independence. Concurrently and independently, researchers have begun to embrace ‘Open Science’ practices (e.g. pre-registration of research questions and hypotheses, open access to materials and data) to foster transparency and create a valid, reliable, and replicable scientific literature. We suggest that Open Science principles and practices can be integrated with existing guidelines for industry-funded research to ensure that the research process is ethical, transparent, and unbiased. In the current paper, we engage with the aforementioned issues and present a formal framework to guide industry-funded research. We outline Guidelines for Research Independence and Transparency (GRIT), which integrates Open Science practices with existing guidelines for industry-funded research. Specifically, we describe how particular Open Science practices can enhance industry-funded research, including research pre-registration, separation of confirmatory and exploratory analyses, open materials, open data availability, and open access to study manuscripts. We offer our guidelines in the context of industry-funded gambling studies, yet researchers can extend these ideas to the behavioral sciences, more generally, and to funding sources of any type.
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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 | 0.775 | 0.836 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.021 | 0.060 |
| Scholarly communication | 0.059 | 0.069 |
| Open science | 0.022 | 0.057 |
| Research integrity | 0.067 | 0.070 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".