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Record W4417526109 · doi:10.25071/1929-8471.166

Benefits of Readers Theatre: An inclusive, participatory, arts-based research approach with youth

2025· article· W4417526109 on OpenAlexaffabout
Lisa Seto Nielsen

Bibliographic record

VenueINYI Journal · 2025
Typearticle
Language
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsData collectionIdentity (music)Citizen journalismYouth engagementParticipatory action researchQualitative researchPositive Youth Development

Abstract

fetched live from OpenAlex

Readers Theatre is an inclusive, arts-based research approach that can be utilized for data collection and data generation. We discuss six benefits of using Readers Theatre in research that was conducted on understanding the experiences of Asian Canadian youth and identity during times of heightened racial tension. The benefits of using Readers Theatre with youth include 1) Readers Theatre privileges the first-voice; 2) it is low-stakes; 3) it is inclusive with direct benefits; 4) it actively engages youth in multifaceted ways; 5) it captures details, feelings, emotions and nuances; and 6) it embodies a participatory approach that enhances validity. Readers Theatre is a novel and engaging approach to qualitative research data collection with youth that offers a way for youth to resist stereotypical discourses and better explore cultural identity within racialized youth. Key words: Readers Theatre, youth, identity, arts-based research.

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)0.029
metaresearch head score (Gemma)0.025
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: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.014
Scholarly communication0.0120.007
Open science0.0020.016
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.813
GPT teacher head0.664
Teacher spread0.148 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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