The Need for Policy Alternatives to Address Alcohol and Other Drug Problems: Developing a Behavioral Risk Insurance Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it