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Record W4417215745 · doi:10.1177/14767503251400127

Moving at the Speed of Trust: A Strengths-Based Analysis of a Participatory Storytelling Project with and for Criminalized Peoples

2025· article· en· W4417215745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAction Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsMcMaster UniversitySimon Fraser UniversityUniversity of British Columbia
FundersVancouver Foundation
KeywordsStorytellingParticipatory action researchCitizen journalismDignityAction (physics)Action researchProcess (computing)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.773
GPT teacher head0.703
Teacher spread0.070 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it