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Record W2030297858 · doi:10.1177/0963947007075979

Foregrounding and refamiliarization: understanding readers' response to literary texts

2007· article· en· W2030297858 on OpenAlexaff
Olívia Fialho

Bibliographic record

VenueLanguage and Literature International Journal of Stylistics · 2007
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsForegroundingIntrospectionDefamiliarizationReading (process)Interpretation (philosophy)Process (computing)FeelingPerspective (graphical)Computer sciencePsychologyLinguisticsAestheticsCognitive psychologyArtSocial psychologyPhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

The present study investigates the effects of foregrounding on the process of defamiliarization of students of literature and engineering, and on the way they develop refamiliarization, that is, the reconstructive process they undergo in order to return to familiar ground. It describes which refamiliarizing strategies these readers make use of and the role of feeling in this process. Data analysis is both quantitative and qualitative. The introspective method of the pause protocol is used in the qualitative part. Here, participants respond to the reading of a short story. The purpose is to investigate how they react to its content and which of its segments trigger comments. Results demonstrate that appreciating the formal elements of a text might be an effective strategy, as readers do not try to decode the text any longer and start reflecting on it, thus building an interpretation. They also develop a new perspective on the world around them and on themselves.

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)0.003
metaresearch head score (Gemma)0.033
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.002
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 head0.020
GPT teacher head0.320
Teacher spread0.300 · 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

Citations60
Published2007
Admission routes1
Has abstractyes

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