Bibliographic record
Abstract
Don’t Believe the Hype is not only the name of a hugely popular song by the hip-hop group Public Enemy but also something I wish more politicians, policymakers, and media pundits had taken into account when crafting, considering, and debating cannabis and its regulation. Had they done so, in my humble opinion, our world would be in a much better place. It was Richard Nixon who declared drug abuse “public enemy number one” at a press conference in 1971 and suggested it was “necessary to wage a new, all-out offensive” to “defeat the enemy.” With those statements, the “war on drugs” was officially launched, bringing billions of dollars (cumulatively, perhaps a trillion dollars to date in the United States alone) to bolster law enforcement and the proliferation of the prison industrial complex. In doing so, innumerable lives have been destroyed, families torn apart, and communities left in tatters. Of course, our prohibitionist approach to drugs and the people who use them dates back much further than Nixon’s declaration. Non-medical use of opium was criminalized in Canada and the United States early in the twentieth century, and cannabis followed shortly thereafter. American anti-drug crusaders took their fight to the world stage, resulting in the adoption of the United Nation’s Single Convention on Narcotic Drugs in 1961. The international treaty controls activities surrounding specific narcotics and stipulates a set of regulations for their medical and scientific uses.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.796 | 0.813 |
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".