Racial Equity in Cannabis Policy: Diversity in the Massachusetts Adult-Use Industry at 18-months
Bibliographic record
Abstract
Background: Cannabis criminalization disproportionately harms communities of color in the United States. In Massachusetts' legal recreational ("adult-use") cannabis industry, state regulations intend to promote diverse participation. We assessed short-term racial/ethnic and gender diversity across the industry and in senior-level positions with greater opportunities to build wealth (i.e., board members, executives, directors). Methods: We extracted race/ethnicity and gender from required registration forms submitted to state regulators for each person working in a licensed adult-use cannabis business from October 2018 to April 2020 (n=4,883). We conducted descriptive analysis and negative binomial regression to assess characteristics associated with senior positions. Results: As of April 2020, racial/ethnic and gender diversity in the Massachusetts adult-use cannabis market (n=4,883) was 75% white, 7% Latino, 6% Black/African American, similar to the state labor market, and 65% male. Diversity was more limited in senior positions. Agents in senior positions (n=403) were 84% white, 2% Latino, 5% Black/African American, and 82% male. Senior-level participation was markedly low for women of color. Conclusion: Despite legislative and regulatory commitment, diversity lacks in senior positions in this emerging cannabis market. States considering adult-use cannabis markets, and those that have already done so, should monitor participation to identify inequities and adapt initiatives to ensure Black/African American and Latino communities socially and economically benefit from state legalization.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".