Integrating open science practices into recommendations for accepting gambling industry research funding
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
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.
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 | 0.029 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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