Bibliographic record
Abstract
The history of psychedelics and why psychedelic stories matter Erika Dyck argues that how people learn about psychedelics today matters, based on the histories of these drugs and how they have been and should be used in clinical medicine. While these substances have piqued the interest and influenced the attitudes of individuals across academia, culture, and medicine, expanding before and well after the 1950's, Dr. Dyck notes that psychedelic drugs have had a long and colourful history. However, their history has not come without polarising opinions, as until recently, trends in cultural attitudes towards non-medical drug use in general and the role of consciousness-altering substances in clinical medicine have often been negative. Despite this writing tradition, more and more public conversations on psychedelics are coming from people with enthusiastic claims about the benefits of psychedelic drug use that rely on severing the current culture of psychedelics from the past, and some might even suggest severing psychedelics from mainstream institutions which tend to be more conservative – like universities or food and drug administrations, or even healthcare systems.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".