Psychonautical engineering: Synergizing the magic of mindfulness, mushrooms, and mindsets for police officer well-being
Bibliographic record
Abstract
The setting of policing exposes its officers to a host of negative health outcomes physiologically, psychologically, and spiritually. Policing mindsets around accessing mental health are far from fixing the epidemic of its mental health crisis or being able to sustain a healthy workforce. Policing is losing the battle with a misguided and a scientifically misinformed war on drugs. Canadian legislators are shifting mindsets from decriminalizing substance use towards applying a public health lens to the mycelium underlying its root causes. So too should its peace officers—not just to restore peace in society—but also in their own minds and in their dysregulated nervous systems by synergizing psilocybin’s neural benefits with mindfulness-based psychotherapy. Western science’s exploration into the healing magic of mushrooms and mindfulness is in its infancy compared with the centuries of wisdom from both Indigenous science and eastern contemplative traditions. Not only does their fusion amplify hope for those suffering but perhaps it offers a scientific key to the neurogenesis of resilience. This is a pracademic trip driven by a retired and now reformed agent from Canada’s War on Drugs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".