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Record W4378419015 · doi:10.1080/13569783.2023.2177145

Transforming relations through oral history performance: restorative justice and the DOHR project

2023· article· en· W4378419015 on OpenAlexafffundabout
Kristina R. Llewellyn, Jennifer Llewellyn, Jennifer Roberts-Smith, G. Morrison, Tony Smith, Tracy Dorrington-Skinner

Bibliographic record

VenueResearch in Drama Education The Journal of Applied Theatre and Performance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsBrock UniversityDalhousie UniversityUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRestorative justiceActive listeningCurriculumOral historyEconomic JusticeNova scotiaSociologyPsychologyPedagogyPolitical scienceCriminologyCommunicationLaw

Abstract

fetched live from OpenAlex

Central to restorative justice is a commitment to sharing and listening to first voice. This is required for the work of transitioning to just relations. The Restorative Inquiry for the Nova Scotia Home for Colored Children (The Home), and its Digital Oral Histories for Reconciliation (DOHR) project, offers a significant example of the power of restorative justice for transitional justice through the performance of oral histories. DOHR has created a curriculum centred on a virtual reality experience of former residents’ oral histories. This paper examines how this curriculum supports restorative justice through a pedagogy of listening and relational scenography.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.018
Scholarly communication0.0080.003
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.089
GPT teacher head0.396
Teacher spread0.308 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes3
Has abstractyes

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