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Record W2068902490 · doi:10.2190/em.28.2.b

An Uncommon Resonance: The Influence of Loss on Expressive Reading

2010· article· en· W2068902490 on OpenAlexaff
Shelley Sikora, Don Kuiken, David S. Miall

Bibliographic record

VenueEmpirical Studies of the Arts · 2010
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReading (process)NarrativePsychologyPerceptionMode (computer interface)LinguisticsLiteratureAestheticsCognitive psychologyArtPhilosophyComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

A study of advanced level English students who read Coleridge's The Rime of the Ancient Mariner indicated that, after a lapse of 2 or more years, impactful loss (e.g., loss due to death) facilitated a mode of reading called expressive enactment. In this mode of reading: (a) stylistic features give narrative objects, characters, and places a sensuous and engaging presence; (b) mutations of the sensuously present “other” occur across striking or evocative reading moments; and (c) the reader becomes metaphorically identified with these transformations in ways that deepen self-perception. The moderating effects of vivid reminiscences on the relationship between loss and expressive enactment suggest that, just as the bereaved gradually become accepting of and perhaps drawn toward occasions that evoke poignant but ephemeral memories of the deceased, so, too, do they become accepting of and perhaps drawn toward the intense interplay between presence and absence that occurs during literary reading.

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)0.002
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.050
GPT teacher head0.387
Teacher spread0.337 · 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 designObservational
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

Citations31
Published2010
Admission routes1
Has abstractyes

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