Bibliographic record
Abstract
F ocusing on emotions in research and writing can help to reveal moments of injustice and legacies of inequity in our society.Despite the importance of emotions to meaning and wellbeing, there are many mechanisms of control in everyday life that discourage us from sharing emotions or talking about them.This kind of silencing or muting eff ect when it comes to emotions are experienced tenfold inside the prison walls.It is therefore hugely important to examine emotions in carceral spaces, as well as in response to criminalization.What the writings in this special issue show is that carceral spaces are a mechanism of hypercontrol, one that discourages people from being their whole selves (also see Fayter, 2023).The prison pathologizes emotions as a main mechanism for reproducing its institutional power and justifying countless punitive 'get tough' laws and policies.Focusing on emotions in carceral spaces and control of emotions or pathologization of emotions is therefore crucial for critical inquiry, as it reveals the injustices of carceral power and the inequality that prison and jails foment.I am not someone who has personally experienced criminalization or incarceration.I have been active with the Journal of Prisoners on Prisons (JPP) for about 15 years.Before that, I was active with Books 2 Prisoners.And for the last decade I have been involved in Walls to Bridges education in Winnipeg on Treaty One Territory.Walls to Bridges, Inside-Out, and other programs like this off ering post-secondary classes inside carceral spaces are really doing transformative work with education.In these diff erent roles over the years, I have encountered the carceral eff ect on emotions in person, in class, on the phone, in letters.Reading this issue, I have thought of those moments, some that had slipped my mind, and I am thankful to these authors for surfacing those memories.This is the background I bring to understanding this topic and that I brought to engaging with the articles in this JPP issue.In my career as a researcher, I have also written a little bit about emotions.In my research I have found that emotions can motivate people to undertake diffi cult tasks and do important work in their communities (Enkhtugs & Walby, 2024).I have found that emotions can help bind people together
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 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".