S1:E4 - From “Tough on Crime” Politician to Justice Reformer - Michael Bryant
Bibliographic record
Abstract
Episode Summary A “tough on crime” politician is locked up and it changes him forever. Michael Bryant exposes how police, prosecutors and courts are wasting time, money, and causing harm - all while there are better ways to keep us safe. Former Attorney General of Ontario, Bryant led the Canadian Civil Liberties Association at the time of this interview. Episode Notes Content Note: policing violence, incarceration, substance use, mental health, suicide, poverty, homelessness, racism. Click here for mental health support resources. Order your copy of Indictment: The Criminal Justice System on Trial (Aevo UTP, 2023) Visit www.benjaminperrin.ca for the latest news and upcoming events We are grateful for support from the University of British Columbia and Law Foundation of British Columbia ----------- Credits: "The Notion" by Northern Points (Intro) "Leva-Eternity" by Lemonmusicstudio Toronto Sun, "Michael Bryant charged in fatal crash" "Racing Forward" by Grayson DeSmet "Newborn Earth" by INPLUSMUSIC "AntSurvila" by RUN CBC The Fifth Estate, "Most killed in police encounters suffered from mental illness, drug abuse issues—CBC research shows" "Chaos In The Outback" by septahelix CityNews, "Vancouver Police Board approves $383 million police budget request" Toronto Sun, "SAFETY AND THE CITY: T.O. mayor candidate Anthony Furey pledges 500 new police officers" CTV News, "Man ticketed after giving change to cop posed as panhandler" "Blue Windows" by Martijn de Boer "The Holy Family Escapes into Egypt" by Gurdonark "Memories of Better Times" by Gurdonark Chris Stewart, "CAHOOTS: Crisis Assistance Helping Out On The Streets" APTN News, "Canada’s prison system has changed little for Indigenous Peoples: Report" "Connect the Dots" by Cosmo Lawson "The Clermont" by Flash Fluharty "A Call to the Soul" by Markotopa "Everyday Adventures" by Sound and Vision (Outtro)
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.677 | 0.282 |
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".