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Record W1974081600 · doi:10.1598/jaal.54.5.2

Which New Literacies? Dialogue and Performance in Youth Writing

2011· article· en· W1974081600 on OpenAlexaff
Kathleen Gallagher, Burcu Yaman Ntelioglou

Bibliographic record

VenueJournal of Adolescent & Adult Literacy · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDialogicLiteracyDramaEthnographyPedagogyPerformative utteranceSociologyCritical literacyMathematics educationPsychologyVisual artsAestheticsArt

Abstract

fetched live from OpenAlex

Measurements of literacy learning in schools, the authors argue, have settled into static and individualized understandings of what should be the most invigorating and social aspect of schooling for youth. By contrast, this article explores the place of aesthetic, dialogic, and performative forms of literacy in the adolescent classroom by excavating ethnographic data from an urban multicultural high school. Close examination of student writing, field notes, and teacher interviews illustrates how the space of the drama classroom creates a laboratory for experimentation with many forms of “new literacies” through rich engagements with the lives inside and the worlds beyond the classroom. The authors reason that drama pedagogies are both creative and critical forms of literacy that offer empirical weight to newer theories of literacy and lead to new modes of theorizing the multiple acts of literacy in schools.

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)0.023
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 score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0090.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.241
Teacher spread0.205 · 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

Citations32
Published2011
Admission routes1
Has abstractyes

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