Confronting the figure of the “mad scientist” in psychedelic history: LSD’s use as a correctional tool in the postwar period
Bibliographic record
Abstract
Since reports about CIA-funded LSD studies came out in the 1970s, psychedelic drugs have invoked images of unethical experimentation and “mad scientists” in the public imagination. Even now, as the stigma surrounding psychedelics diminishes in the 21st century, the figure of the “mad scientist” continues to occupy a space in what Ido Hartogsohn calls the “collective set and setting,” the larger framework of cultural understandings that shape how individuals experience psychedelic drugs. Scientists and humanities scholars who study these drugs have responded to this issue by drawing boundaries between those who used psychedelics carefully and those who used them ignorantly. Yet these boundaries were not always so clear in the past. Drawing on historical examples of LSD’s use as acorrectional toolin Canada, I show how enthusiasm about the drug’s potential led several experienced and knowledgeable psychedelic therapists to use it on vulnerable populations in diverse institutional settings, such as correctional facilities. These examples reveal how the institutional context of modern industrial societies shaped the application of psychedelic therapy in the past and suggest that today’s therapists need to carefully consider how this broader context impacts their work.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.040 | 0.091 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".