Bibliographic record
Abstract
Objectives: Using the same collaborative, evidence-driven approach that produced Canada’s Low Risk Alcohol Drinking Guidelines, this project aims to develop a workable set of national Lower-Risk Gambling Guidelines (LRGGs) with clear quantitative limits describing when level of gambling involvement is more likely to result in individual harms. These guidelines will help people make informed decisions about their gambling.\nMethods: In April 2016, a scientific working group was formed and tasked with synthesizing available evidence from two Canadian and eight international population datasets (from the United States, Iceland, Norway, Sweden, France, Australia, and New Zealand) on the relationship between gambling involvement (i.e., frequency, expenditure, and duration) and gambling related harms (i.e., financial, relationship, emotional, and physical harms). A national advisory committee, including partners from government and industry, was formed to review the evidence and oversee the development of the LRGGs.\nResults: In late 2018, preliminary LRGGs were developed, presented and discussed with a team of international collaborators and the national advisory committee. These preliminary limits will be presented. A final technical report detailing the final guidelines, the evidence that informed their development, limitations, and essential contextual factors will be published in March 2020.
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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 | 0.094 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".