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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".