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Record W4229368359 · doi:10.1177/1086296x221098068

Private Readerly Experiences of Presence: Why They Matter

2022· article· en· W4229368359 on OpenAlexaff
Margaret Mackey

Bibliographic record

VenueJournal of Literacy Research · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReading (process)Set (abstract data type)MandatePoint (geometry)SociologyEpistemologyPsychologyLinguisticsPhilosophyPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

This article draws on Philip Barnard's model of the interactions between theory and practice, between basic and applied research, to investigate the paradox of reading as an experience both private and public. It uses internal reader experience as a starting point for exploration, evoking the concept of a readerly sense of presence as a selection criterion. Investigating chapters in two novels for young readers, Northern Lights (The Golden Compass) by Philip Pullman and The Moffats by Eleanor Estes, and drawing on cognitive models of reading, it analyzes the textual constructs that set up a potential for the kind of enactive resonance that enables (though does not mandate) a sense of presence. It investigates the methodological implications of an enhanced sense of reading as a non-reproducible experience and considers the policy and pedagogical implications of not restricting public concepts of reading to what can be readily measured or repeated.

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)0.038
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 score0.013
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.031
Scholarly communication0.0130.021
Open science0.0010.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.097
GPT teacher head0.382
Teacher spread0.286 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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