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Record W2012907922 · doi:10.1111/1540-4781.00189

Higher–Level and Lower–Level Text Processing Skills in Advanced ESL Reading Comprehension

2003· article· en· W2012907922 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueModern Language Journal · 2003
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWord recognitionReading comprehensionComprehensionReading (process)Computer scienceLinguisticsPsychologyNatural language processingArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigated the role of higher–level syntactic and semantic processes and lower–level word recognition and graphophonic processes in adult English as a second language (ESL) reading comprehension. In particular, the study examined the extent to which these processes can discriminate skilled from less–skilled readers in a sample of fairly advanced ESL readers. Measures of reading comprehension, syntactic, semantic, word recognition, phonological, and orthographic processing skills were used. One–way discriminant function analysis revealed that lower–level component processes, such as word recognition and graphophonic processes, in addition to higher–level syntactic and semantic processes, contributed significantly to the distinction between skilled and less–skilled ESL readers. These findings suggest that efficient lower–level word recognition processes are integral components of second language reading comprehension and that the role of these processes must not be neglected even in highly advanced ESL readers.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.888
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.319
Teacher spread0.293 · 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