Bibliographic record
Abstract
The benefits of engaging in theatre- and music-making have been well proven for various populations. (see Črnčec et al. 2006; Lehmberg and Fung 2010; Salur et al 2017, etc.) These benefits are particularly significant for individuals who have experienced trauma, especially incarcerated individuals. (see Kyprianides and Easterbrook 2020; Reid 2019, etc). Music and theatre programs vary in Canada, and are present in many Canadian prisons. In this paper, I examine two programs more closely: the grass roots program Pros and Cons at The Joyceville Institution in Kingston, Ontario, which involves a collaboration between volunteer musicians and a group of incarcerated men, and Diane Conrad’s work with a young offender’s facility in Alberta Canada, where she employed devised theatre techniques to create meaningful theatrical pieces within the prison’s walls. Both these programs are working towards a similar goal: preparing the incarcerated individuals to return to society through practicing and rehearsing healthy community and citizenship through collaborative music and theatre. While this is an admirable goal for this work, the conversation surrounding music and theatrical work in prisons has been focused on its rehabilitative aims and properties. In this presentation I will explore the features of both programs, examine the rehabilitation goals and the focus on rehabilitation in the literature on prison-based music and theatre programs, and discuss ways that Transformative Justice and the Abolitionist movement can be supported through these arts-based initiatives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.036 | 0.009 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".