The Effect of Marijuana Legalization on Anticipated Use: A Test of Deterrence Theory
Bibliographic record
Abstract
Marijuana is the most frequently used illicit drug in the world (Erickson, Van Der Maas, and Hathaway, 2013:428). Here in the United States, public support for the legalization of marijuana for recreational use is substantial. With public support, both Colorado and Washington passed state initiatives in 2012 to legalize recreational use of marijuana for individuals aged 21 years and older. Even the federal government has recently reversed their initial position to continue to enforce federal drug laws within these states. With what appears to be increasingly liberal attitudes toward marijuana use and even toward legalization, some are concerned about what this may mean for drug use in America. To many, it appears obvious that with changing attitudes and more lenient policies, use of marijuana will increase and in turn exacerbate a host of individual and societal problems that marijuana use is thought to cause. The primary focus of this study examines the first part of these concerns: to what extent will marijuana use increase with these policy changes? Specifically, this research looks at what extent current abstainers of marijuana might use if it were legalized.
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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 | 0.015 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".