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Record W2987074823 · doi:10.1177/1532708619885413

A Walk in the Park: A Virtual Workshop Exploring Intertextuality and Implicit Powers Within Texts

2019· article· en· W2987074823 on OpenAlexafffund
Joe Norris

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntertextualityPresentational and representational actingOralityContext (archaeology)The artsMeaning (existential)Computer scienceReading (process)Variety (cybernetics)SituatedPoetryGestureOral poetryLinguisticsSociologyMultimediaAestheticsLiteratureVisual artsArtEpistemologyArtificial intelligenceHistoryPhilosophyLiteracyPedagogy

Abstract

fetched live from OpenAlex

Focusing on a variety of possible presentational styles of a personal poem, this article explores the problematics of the interplay of the medium and the message. It brings in the theoretical concepts of intertextuality (how previous knowledge impacts one’s reading), media(tion) (how the choice of dissemination through printed texts, spoken texts, images, or combination of these changes the content’s meaning), presence (how readers and/or audience members are situated ontologically and axiology, depending on whether the presentations are recorded or live), the degree of specificity (how detailed information influences interpretative stances), sequencing (how to provide alternatives to the canon of traditional, linear, expository writing), contexture (how the presence of images, gestures, and sounds provides textures to the context examined) and arts-based dissemination (moving beyond flat, black and white printed texts through weblinks). Structured as a virtual workshop based on ones that I have presented, it provides writers and readers with a range of possible choices of how to engage with/in poetic inquiry.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.199
GPT teacher head0.451
Teacher spread0.252 · 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.

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

Citations5
Published2019
Admission routes2
Has abstractyes

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