Correcting the record on NCRG‐funded research
Bibliographic record
Abstract
We are writing to respond to an editorial in Addiction [1] that made reference to the National Center for Responsible Gaming (NCRG) and its research grants program.The editorial is misleading, as it provides a mischaracterization of our program.We are responding to provide a realistic picture of NCRG.When discussing NCRG-funded publications in their essay, Babor & Miller state that 'fewer than half (48%) cited funding from the NCRG in their Acknowledgement section or in a footnote of the paper' ([1], p. 341).We were surprised at this low rate of acknowledgement.Consequently, the NCRG staff contacted Dr Babor, who provided a list of the 30 papers included in his study as well as the 173 papers from which the sample of 30 was drawn.We examined the 167 papers from Babor & Miller's population that were immediately available to us and found that 72% of papers acknowledged that funding was provided by NCRG or the correct name of the organization at the time of paper publication.Also, in the past 5 years more than 98% of NCRG-funded papers have been acknowledged correctly.The 48% acknowledgement rate observed by Babor & Miller in their sample missed one out of every three acknowledgements that were available in the population.We suspect that Babor & Miller's rate was inaccurate for two main reasons. 1 None of the 30 papers in the sample was selected from the most recent 4 years (2009-12) of publications.According to our calculations, the probability of selecting 30 studies randomly from a population of 173 without selecting any of the most recent 51 papers is approximately 0.0000083, or approximately one in 120 000. 2 The authors did not count a paper that acknowledged a previous name used by the NCRG as acknowledging the NCRG (Institute for Research on Pathological Gambling and Related Disorders was the name of NCRG's research function when based at the Division on Addiction, Cambridge Health Alliance).We have included our complete materials supporting the above points for public inspection in the 'Supporting information' section of the Journal's website.The claim that 'fewer than half ' of NCRG-funded papers were acknowledged is not accurate.The actual rate for the history of the organization is more than 70% and more than 98% in the last 5 years.Correcting this misleading claim is important to NCRG, the Scientific Advisory Board and NCRG grantees.The claim leads readers to think mistakenly that NCRG is exerting influence on the science it funds, and that NCRG awardees do not want to divulge the source of funding for fear that their science will not be published.This could not be further from the truth.
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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 | 0.144 | 0.635 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.030 | 0.036 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.032 | 0.016 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.023 | 0.040 |
| Insufficient payload (model declined to judge) | 0.023 | 0.024 |
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".