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Reader and text factors in reading comprehension processes

2010· article· en· W2147003560 on OpenAlexaff
Panayiota Kendeou, Krista R. Muis, Sandra M. Fulton

Bibliographic record

VenueJournal of Research in Reading · 2010
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComprehensionPsychologyReading comprehensionReading (process)Conceptual changeThink aloud protocolCognitionFunction (biology)LinguisticsCognitive psychologyComputer scienceMathematics educationPhilosophy

Abstract

fetched live from OpenAlex

The effects of epistemic beliefs and text structure on cognitive processes during comprehension of scientific texts were investigated. On‐line processes were measured using think‐aloud (Experiment 1) and reading time (Experiment 2) methodologies. Measures of off‐line comprehension, prior knowledge and epistemic beliefs were obtained. Results indicated that readers adjust their processing as a function of the interaction between epistemic beliefs and text structure. Readers with misconceptions and more sophisticated epistemic beliefs engage in conceptual change processes, but only when reading refutation texts. Results also showed that memory for text is not affected by differences in epistemic beliefs or text structure. These findings contribute to our understanding of the relations among factors associated with text comprehension and have implications for theories of conceptual change.

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)0.004
metaresearch head score (Gemma)0.068
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.068
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.202
GPT teacher head0.488
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 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

Citations118
Published2010
Admission routes1
Has abstractyes

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