Effects of Regulation Intensity on Marijuana Black Market After Legalization
Bibliographic record
Abstract
Since 2012, many states and Canada have legalized the use and sale of recreational marijuana. One of the expected benefits of the legalization is that the establishment of a legal cannabis market would eliminate the black market which has been the main form of marijuana trade for decades. Even though legal options are available for marijuana producers and consumers, the black market is still thriving in states where recreational marijuana has been legalized. The reasons behind the persistence of the marijuana black market are complex. One of the main arguments is that the legalized states have failed to establish a regulatory framework which effectively keeps both producers and consumers in the legal market. Instead, strict regulations and high cost of compliance have created an environment in favor of big players while driving small-scale businesses into the black market. The current research attempts to study this issue by answering the research question of whether overregulation is pushing some marijuana businesses back to the black market or preventing them from entering the legal market. This thesis employs a mix-method design to analyze qualitative data of news articles reporting the reasons that marijuana businesses decide to stay in the black market and a quasi-experimental time series analysis of National Incident-Based Reporting System (NIBRS) data regarding marijuana offenses in Colorado and Washington between 2014 and 2017. The qualitative analysis of news reports reveals that regulation is one of the main reasons that people stay in the illicit market. The comparison of marijuana crime trends in Colorado and Washington shows mixed findings. While marijuana offense rates in Colorado largely remained steady over the years, those in Washington increased dramatically after the implementation of more intensive regulations. The results of this study have several policy implications for the marijuana legalization as well as implications for future research on the black-market issue.
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.
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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".