A framework for independent research using industry funding: the Massachusetts model
Bibliographic record
Abstract
The Massachusetts Model represents an approach to funding gambling research that addresses concerns about industry influence while advancing evidence-based policy and harm reduction initiatives. Developed by the Massachusetts Gaming Commission (MGC), this model integrates mandatory industry fees, an open procurement process, and a robust commitment to open science principles. The Massachusetts Model aligns research with public health priorities and ensures rigorous oversight through its Research Review Committee. This paper explores the model’s development, its unique legislative provisions – including access to player-level data – and its impact on advancing gambling research and policy. Comparative analyses highlight the advantages and limitations of alternative funding approaches worldwide. We contend that the Massachusetts Model offers a viable path forward for other jurisdictions seeking to engage in rigorous scientific inquiry using indirect industry funding, while maintaining independence and transparency. In doing so, it addresses broader challenges in securing sustainable funding for independent research in the field of gambling studies, providing a framework that prioritizes public health and ethical governance in the research process and its outcomes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Incentives · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.035 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.027 | 0.049 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".