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Record W1603691209 · doi:10.37119/ojs2014.v20i2.160

You Don’t Know Me: Adolescent Identity Development Through Poetry Performance

2014· article· en· W1603691209 on OpenAlexaffvenueabout
Janette Hughes, Laura Morrison, Cornelia Hoogland

Bibliographic record

Venuein education · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsWestern UniversityOntario Tech University
Fundersnot available
KeywordsPoetryIdentity (music)CriticismInterrogativeSpoken wordPsychologySociologyCoupletPedagogyLiteratureArtAestheticsLinguistics

Abstract

fetched live from OpenAlex

Our study concerns adolescents using poetry writing as an interrogative and creative means of shaping and creating “voices” or “identities.” Toronto-based high school students were challenged to be creators (rather than solely consumers) of available social practices within a digital landscape using mobile devices and social networking platforms. The students engaged in the processes of creating poetry that included experimentation with form (including spoken word, found, and rhyming couplet poetry), research, and writing-induced challenges of received ideas. Their creations of their multiple “Resonant Voices,” which in some cases were powerful statements of self-discovery and social criticism, were further amplified because they occurred in a formal educational setting.Keywords: adolescents; identity; digital literacies; multiliteracies; poetry; social practices; social networking sites; Facebook; pedagogy; mobile devices; Android app; poetic inquiry; metacognitive

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)0.004
metaresearch head score (Gemma)0.008
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.268
Teacher spread0.246 · 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

Citations1
Published2014
Admission routes3
Has abstractyes

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