The Need for Policy Alternatives to Address Alcohol and Other Drug Problems: Developing a Behavioral Risk Insurance Model
Bibliographic record
Abstract
Current approaches to funding addictions are critiqued as being fundamentally flawed and inadequate, in spite of the fact that disproportionate amounts of revenues come from the most vulnerable users of alcohol and other drugs. A new approach is proposed, constructing alcohol and other drug use as risky behavior for which users could be insured based on the amount they consume. By adding 5 cents to the cost of every standard drink consumed, the current investment in Ontario, which is spent almost exclusively on treatment services, would be doubled, allowing for funding to be extended to prevention and research initiatives. Ontario's problem gambling strategy, where a percentage of gambling revenues is used to fund treatment, prevention and research into gambling problems, illustrates the potential of such an approach. The behavioral risk insurance model offers a novel way of addressing alcohol and other problems, and could be extended to a wide range of behaviors that carry the risk of harm because of their addictive potential.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".