Moving at the Speed of Trust: A Strengths-Based Analysis of a Participatory Storytelling Project with and for Criminalized Peoples
Bibliographic record
Abstract
In this paper we describe the considerations, processes and resulting insights of a trauma-informed Participatory Action Research (PAR) storytelling workshop which aimed to support healing and dignity through strengths-based writing and storytelling with and for people who have been incarcerated in British Columbia (BC), Canada. Activist scholars walked alongside Facilitators and participating Storytellers as a mechanism to offer supports and welcome feedback for continual improvement. This paper describes processes and shares insights from Facilitators and Storytellers on the processes’ impacts on individual and collective healing and wellbeing, including concrete ways to create time for relational processes, the risks and potential harms of storytelling about personal trauma and experiences of injustices, and the impacts of COVID-19. Implications for PAR scholars, community organizers and storytelling programmers are shared.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".