Bibliographic record
Abstract
This non-peer reviewed interview, originally published on The Grey Lit Café podcast, is published as part of the Critical Gambling Studies blog. CitationSponsored reports: David Baxter on the interface between research and policy. The Grey Lit Café. https://thegreylitcafe.buzzsprout.com/1936705/10829288-sponsored-reports-david-baxter-on-the-interface-between-research-and-policy DescriptionA significant portion of gambling research funding comes from non-academic sponsors—mainly governments or government-organized bodies — and the output of the sponsored project is usually a research report to the sponsor rather than academic journal articles or books. Research published in this way is of comparable quality to academic publications, but is referred to by librarians and information managers as "grey literature" because its limited distribution can make it difficult to discover and manage.Many academic journal articles on gambling are in fact spin-offs that originated from such sponsored projects. Researchers adapt their work into academic articles to reach new audiences and build the academic body of knowledge, but also because grey literature contributions receive much less recognition in academics' career evaluations.In this episode of The Grey Lit Café, David Baxter has a critical discussion with host Anthony Haynes about the challenges gambling researchers face when doing sponsored research, how the conflicts of interest of sponsored research shape the academic body of knowledge on gambling, and ways that gambling researchers and government sponsors can better support each other's needs as well as the needs of people experiencing gambling harms whom the sponsored research is intended to help.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.729 | 0.613 |
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".