Bibliographic record
Abstract
The Journal of Prisoners on Prisons (JPP) invites submissions for a special issue on the theme of "Emotions and Carceral Spaces".Experiences of imprisonment or living and working in various carceral settings are isolating, punitive, and at times traumatic, all of which infl uence the emotional experiences of both prisoners and staff .Carceral spaces are neither uniform nor orderly, and the way emotions are felt and expressed diff ers signifi cantly depending on the specifi c setting, lived experiences, and interpersonal interactions.Diff erent carceral environments can produce multiple emotional experiences, which can also diff er based on gender, race, sexuality and other markers of diff erence.Individuals' age, past experiences, length of sentence, and security level can also impact one's emotions.We encourage authors to share and critically refl ect on their emotional experiences within carceral environments or how diff erent physical spaces in jails, prisons, treatment centres, detention centres, psychiatric facilities, halfway houses or other sites of confi nement aff ect prisoners' moods and behaviours.Submissions that refl ect how emotions are organized and expressed in prison, along with where and how it is appropriate to express oneself emotionally in the culture of prison and the policy context are especially welcomed.We hope to better understand how carceral spaces can shape people's emotional experiences, while also impacting or being impacted by interpersonal relations among prisoners, as well as between prisoners and staff .Additionally, we invite submissions concerning how emotions contribute to prison spaces being perceived as a heightened 'HIV risk environment'.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.000 |
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".