Bibliographic record
Abstract
Despite growing evidence to the contrary, researchers continue to posit causal links between cannabis, crime, psychosis, and violence. These spurious connections are rooted in history and fueled decades of structural limitations that shaped how researchers studied cannabis. Until recently, research in this area was explicitly funded to link cannabis use and harm and ignore any potential benefits. Post-prohibition cannabis research has failed to replicate the dire findings of the past. This article outlines how the history of controlling cannabis research has led to various harms, injustices, and ethical complications. We compare commonly cited research from both the prohibition and post-prohibition eras and argue that many popular claims about the dangers of cannabis are the result of ethical lapses by researchers, journals, and funders. We propose researchers in this area adopt a duty of care in cannabis research going forward. This would oblige individual researchers to establish robust research designs, employ careful analytic strategies, and acknowledge limitations in more detail. This duty involves the institutional recognition by funders, journals, and others that cannabis research has been deliberately misconstrued to criminalize, stigmatize, and pathologize.
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 | 0.396 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.149 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.026 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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".