Acknowledgements
Bibliographic record
Abstract
We want to begin by acknowledging the Treaty 4 territory where the Weyburn Mental Hospital once stood and where this book unfolded.This book is the product of the inspiration, reflection, collaboration, and ultimately hard work of many people who have all encountered the mental health system in different ways.We all have our reasons for getting involved in writing the book, and we all have different people and experiences to thank for that motivation.What initially began as a more modest proposal from Hugh Lafave to write up a little something on the remarkable experiment(s) that unfolded in Saskatchewan gradually grew in scope, and we quickly realized that the strength of our venture multiplied as we joined with others who helped to nuance and enrich the discussion.After several telephone calls, and a few visits to Ontario, Hugh introduced me, Erika Dyck, to Gary Gerber, and our circle widened.We started by applying to the Saskatchewan Health Research Foundation for a new investigator grant to support the hiring and training of an archivist who could take the newly deposited patient admission records and enter them into a database, which we could then read, compare, and analyze to tease out patient experiences in the first half of the twentieth century.Alex Deighton took a course on the history of madness with me and the following summer volunteered to work in the archives retracing the earlier history of the hospital.After many weeks reviewing institutional materials and government records, he began writing up his impressions, and it was clear that he was going to be an integral part of the team because of his sophisticated research and sensitive analysis.Ultimately, Alex took the lead on Chapters 1 to 3.Alex Dyck was a medical student at the University of Saskatchewan when he approached me about doing a summer research project.We applied for funding from Associated Medical Services and received a student internship grant for Alex to compare diagnostic categories over time and through different nascent systems of classification.Along the way, I met John Mills through correspondence and ultimately collaborated with him on a paper.John was working on his own manuscript about the history of psychiatry in Saskatchewan.Before he completed it, however, he fell ill and passed it along to the team, allowing us to use parts of it for our own purposes.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.459 | 0.341 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".