MétaCan
Menu
Back to cohort

Taken out of context: differential processing in contextual and isolated word reading

2011· article· en· W2161243018 on OpenAlexafffund
Sandra Martin‐Chang, Kyle Levesque

Bibliographic record

VenueJournal of Research in Reading · 2011
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyReading (process)Task (project management)Context (archaeology)Cognitive psychologyWord (group theory)CognitionRecallSurpriseLinguisticsCommunication

Abstract

fetched live from OpenAlex

Three experiments are reported that investigate the cognitive processes underlying contextual and isolated word reading. In Phase 1, undergraduate participants were exposed to 75 target words under three conditions. The participants generated 25 words from definitions, read 25 words in context and read 25 in isolation. In Phase 2, volunteers completed either an explicit recall task (Experiment 1), an implicit word stem completion task (Experiment 2) or both tasks (Experiment 3). Our findings provide converging evidence that contextual and isolated word reading elicit different patterns of cognitive processing. Specifically, Experiments 1–3 demonstrated that words read in context were remembered similarly to words generated from definitions: words from both conditions were recalled more frequently in the surprise memory task and selected less often to complete the word stems in the implicit memory task. The opposite pattern was noted for words read in isolation. Reading in context is discussed in terms of its greater reliance on semantic processing, whereas isolated word reading is discussed in relation to perceptually driven processes.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.145
GPT teacher head0.423
Teacher spread0.278 · 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

Citations12
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Research in ReadingSame topicReading and Literacy DevelopmentFrench-language works237,207