When prohibition works: Comparing fireworks and cannabis regulations, markets, and harms
Bibliographic record
Abstract
BACKGROUND: Nations wrestle with whether to prohibit products that can harm consumers and third parties but whose prohibition creates illegal markets. For example, cannabis is banned in most of the world, but supply for non-medical use has been legalized in Uruguay, Canada, and much of the United States and possession restrictions have been liberalized in other countries. Likewise, supply and possession of fireworks have been subject to varying degrees of prohibition in multiple countries, with those bans prompting significant evasion. METHODS: Current and past history of fireworks regulations, sales, and harms are reviewed and contrasted with those for cannabis. The focus is on the United States, but literature from other countries is incorporated when possible and appropriate. This extends the insightful literature comparing drugs to other vices (such as gambling and prostitution) by comparing a drug to a risky pleasure that is not seen as a vice but which has been subject to prohibition. RESULTS: There are many parallels between fireworks and cannabis in legal approaches, harms to "users", harms to others, and other externalities. In the U.S. the timing of prohibitions were similar, with prohibitions on fireworks being imposed a little later and repealed a little sooner. Internationally, the countries that are strictest with fireworks are not always those that are strictest with drugs. By some measures, harms are of roughly similar magnitude. During the last years of U.S. cannabis prohibition, there were about 10 emergency department (ED) events per million dollars spent on both fireworks and illegal cannabis, but fireworks generated very roughly three times as many ED events per hour of use/enjoyment. There are also differences, e.g., punishments were less harsh for violating fireworks prohibitions, fireworks consumption is heavily concentrated in just a few days or weeks per year, and illegal distribution is primarily of diverted legal products, not of illegally produced materials. CONCLUSIONS: The absence of hysteria over fireworks problems and policies suggests that societies can address complex tradeoffs involving risky pleasures without excessive acrimony or divisiveness when that product or activity is not construed as a vice. However, the conflicted and time-varying history of fireworks bans also show that difficulty balancing freedoms and pleasure with harms to users and others is not restricted to drugs or other vices. Use-related harms fell when fireworks were banned and rose when those bans were repealed, so fireworks prohibitions can be seen as "working" from a public health perspective, but not well enough for bans to be employed in all times or places.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".