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Record W2769683709 · doi:10.1111/lit.12134

Failing with grace: kids, Improv and embodied literacies

2017· article· en· W2769683709 on OpenAlexafffund
Kimberly Lenters, Alec Whitford

Bibliographic record

VenueLiteracy · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEmbodied cognitionLaughterLiteracyAssemblage (archaeology)MaterialismComedyGesturePedagogyCritical literacySociologyPsychologyMathematics educationVisual artsLinguisticsEpistemologySocial psychologyArtHistory

Abstract

fetched live from OpenAlex

Abstract In this article, we explore the idea that comedy, with its often unorthodox ways of looking at, experiencing, and responding to the world, offers untold possibility for classroom literacy instruction. The article focuses on the potential of Improv comedy as socio‐materialist literacy in the classroom. It provides an account of Improv as a form of embodied literacy that operates as an assemblage created collectively between many people, practices, and material objects. We present findings from interviews with professional comedians regarding the possibilities of comedy for language and literacy instruction with elementary school children. The article then examines a moment from the subsequent classroom phase of the study to look at ways Improv can help students create stories and ways that laughter can be used to create a cohesive assemblage based around students' spontaneous creation of texts. The aim of the article is to provide educators with a practical means to apply socio‐materialist literacy in their classrooms through Improv, which will, in turn, allow students to create collectively generated texts and assemblages.

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)0.003
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 score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0080.027
Scholarly communication0.0090.006
Open science0.0010.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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