Bibliographic record
Abstract
I fi rst met Sarah in the spring of 2018 when she was looking to get involved in a group that began its work over a decade ago called the Criminalization and Punishment Education Project, which aims to reduce the use and harms of policing and imprisonment, as well as build safer communities through research on and advocacy for alternatives.At the time, Sarah was new to Ottawa and, with a background of research and advocacy on overdose prevention sites, she was looking to get involved in community organizing to work in solidarity with people who struggle to meet their basic needs and, in the process, enhance our collective well-being and safety.Early conversations with Sarah led to the creation of the Jail Accountability & Information Line in December 2018.Sarah played a key role in launching this initiative and running the hotline that took thousands of calls from people imprisoned at the Ottawa-Carleton Detention Centre in its early years.Through this initiative, Sarah worked with imprisoned people on various human rights and re-entry issues they faced, including getting access to medical care behind and beyond bars.For instance, I recall the early days of the hotline where Sarah literally spent days advocating for a person who had fallen off the bunk in their jail cell to the concrete fl oor and broke their leg to get transferred to a hospital so that he could get examined and get access the care he needed that was being denied by jail staff and management who did not believe him.I also recall numerous instances where Sarah arranged for doctor's appointments for people coming out of the jail to ensure that their opioid substitution prescriptions would continue once they were released so that they would not turn to street drugs, and potentially overdose and die.There is no doubt Sarah saved lives that through her community organizing and advocacy.In 2019, Sarah became Dialogue Editor for the Journal of Prisoners on Prisons, which is a section aimed at bringing various people with lived expertise of imprisonment in conversation with each other to advance thought and praxis on a particular challenge facing prisoners.Among the contributions Sarah made in this role was the publication of Volume 28, Number 2 of the journal featuring a dialogue on "Prison (In)justice in Canada at the Crossroads", which served as a clarion call for changes to federal imprisonment in the country that remain needed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.017 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.030 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".