Will psychedelic research and the ‘psychedelic renaissance’ create another generational divide?
Bibliographic record
Abstract
Will psychedelic research and the ‘psychedelic renaissance’ create another generational divide? Erika Dyck, Canada Research Chair in History of Health & Social Justice at the University of Saskatchewan, discusses changing attitudes to psychedelics and the challenges in forming a strong evidence base from available psychedelic research. As psychedelics re-emerge in the 21st century, their storied past continues to haunt their future. Tales have circulated of their entanglement in unethical medical research and implication in violent behaviours, including suicides and homicides. Psychedelics have even been blamed for turning an entire generation of young people to question authority and the values of a post-World War culture. Are psychedelics really to blame for these social divisions? I do not think that psychedelics are the problem, but they may be a symptom.
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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 | 0.066 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".