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Record W3174075023 · doi:10.1002/jaal.1178

Spoken Word Poetry with Multilingual Youth from Refugee Backgrounds

2021· article· en· W3174075023 on OpenAlexfundno aff
Jennifer Burton, Saskia Van Viegen

Bibliographic record

VenueJournal of Adolescent & Adult Literacy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSpoken wordContext (archaeology)RefugeePoetryLiteracyLinguisticsSpoken languageSituatedEmbodied cognitionPedagogySociologyPsychologyHistoryComputer science

Abstract

fetched live from OpenAlex

Abstract This article reports insights from a spoken word poetry workshop conducted with youth from refugee backgrounds and their teachers at a large suburban secondary school. The broad aim of engaging spoken word poetry in the educational context was to encourage and listen to student voice and creative self‐expression, and to explore opportunities within the class to speak to sociopolitical issues and injustices relevant to students’ lives and experiences. The article illustrates how the workshop created situated, embodied teaching and learning activities to familiarize students with the spoken word genre and invite students to imagine spoken word as part of their expanding repertoire. These experiences engaged with students’ linguistic, cultural, and other semiotic resources, valorizing translanguaging as a critical practice for youth in the process of resettlement and teachers supporting their social and educational integration. The article concludes with implications for teachers interested in incorporating spoken word in the classroom.

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)0.005
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: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0150.008
Scholarly communication0.0090.003
Open science0.0010.014
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.401
Teacher spread0.363 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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