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Record W2990363323 · doi:10.1002/jaal.1034

“How Emotional Do I Make It?”: Making a Stance in Multimodal Compositions

2020· article· en· W2990363323 on OpenAlexfundno aff
Jennifer Rowsell

Bibliographic record

VenueJournal of Adolescent & Adult Literacy · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAffordanceComposition (language)Construct (python library)Affect (linguistics)Rhetorical questionRepresentation (politics)Expression (computer science)LiteracyPortraitSociologySpace (punctuation)Frame (networking)PsychologyObject (grammar)PedagogyAestheticsLinguisticsVisual artsCognitive psychologyArtCommunicationComputer scienceLiteraturePolitics

Abstract

fetched live from OpenAlex

Abstract For literacy educators, there is a need to understand students’ pathways into composition and mediate contemporary, multimodal compositional pathways with more academic ones. In an effort to mediate between middle and high school students’ schooling and curricular demands and their everyday interests and investments in media and communicational systems, the author offers educators a way to frame composition that attends to the potential and affordances of multiple modes of expression and representation. Combining affect theory with Arendt's writings on thinking and embodiment, the author presents a research study with adolescents who made stances in selfies, self‐portraits, and written artist statements that are indicative of new rhetorical and compositional practices. Stance, as a construct in modern compositions, represents the ways that young people interface with ideas and experiences within the world that materialize in and animate their designs. Stance provides young people with a space to tell the stories they want to tell through media and mediums of their choosing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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 teacher head, 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

Citations21
Published2020
Admission routes1
Has abstractyes

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