From Stigma to Support: “Black-Owned” Labels and Expertise Stereotypes in Cannabis and Psychedelics Markets
Bibliographic record
Abstract
Abstract We examine the effect of “Black-owned” labeling on cannabis and psychedelic brands, in context of the stigmatized and risky nature of the drugs category. Building on prior studies examining social justice or discrimination, we introduce an expertise stereotype framework. As study 1, we surveyed 37 Black professionals in the drugs industry about expectations regarding “Black-owned” labels, as juxtaposition to consumer responses in the following studies. In study 2, we measured expertise stereotypes about Black and women entrepreneurs across various product types. Utilizing these findings, we contrasted the effect of “Black-owned” labels on cannabis versus candy products in study 3. “Black-owned” labeling increased Black participants’ intentions to consume candy, but not cannabis; and the pattern reversed for White participants such that “Black-owned” labeling increased their intentions only for cannabis. Whereas out-group members’ response is consistent with expertise stereotypes, in-group members’ support does not extend to the stigmatized category. In study 4, field ad campaigns revealed that “Black-owned” (vs. no) label increases click-through by 21% on a psychedelics ad, while a “Woman-owned” (vs. no) label reduces by 15%, consistent with expertise stereotypes. These findings advance the discourse on ownership labeling and provide insight into nuanced consumer responses in this category.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".