Pot for Profit: Cannabis Legalization, Racial Capitalism, and the Expansion of the Carceral State <i>by Joseph Mello</i>
Bibliographic record
Abstract
The legalization of cannabis in many U.S. states seemed like a happy ending after the failure of the War on Drugs. Yet, as Mello demonstrates in Pot for Profit, the benefits of this new “green rush” (56) have not been equitably divided. Mello employs a sociolegal approach to explain how the legalization of cannabis, rather than putting an end to the battle of the cannabis community against the forces of prohibition, instead represents an “act of creative destruction” (21). This new regulatory regime uses legalization to enact less overtly harmful—but perhaps more insidious—forms of state control. Drawing on the law and society and social movements scholarship, theories of racial capitalism, as well as Foucault's biopolitics, Mello demonstrates how cannabis legalization left behind the primarily racialized and economically marginalized people impacted by the mass incarceration and overpolicing of the War on Drugs. The book gives a comprehensive overview of the sociolegal history of cannabis in the United States. The story is then brought into the present through interviews with cannabis activists and extensive textual analysis. The interviews are effective at illustrating Mello's argument and putting a human face on the history and politics of cannabis regulation, something so often missing from policy discussions. Mello's empathy for his participants shines through the pages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".