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Record W1892609784 · doi:10.1111/1467-9817.12048

Metacognitive online reading strategy use: Readers' perceptions in L1 and L2

2015· article· en· W1892609784 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Research in Reading · 2015
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)PsychologyMetacognitionPerceptionTest (biology)Mathematics educationCognitionLinguistics

Abstract

fetched live from OpenAlex

This study aimed to explore whether first‐language (L1) readers of different language backgrounds would employ similar metacognitive online reading strategies and whether reading online in a second language (L2) could be influenced by L1 reading strategies. To this end, 52 Canadian college students as English L1 readers and 38 Iranian university students as both Farsi L1 and English L2 readers were selected. After completing three reading tasks on the Web, their perceptions about their use of strategies were assessed via a survey of reading strategies. Analyses of the data, using an analysis of variance and the Scheffé post hoc test, revealed certain differences. The Canadian readers perceived themselves to be high‐strategy users employing mostly a top‐down approach, whereas the Iranian readers in both Farsi and English appeared to be medium‐strategy users, favouring mostly a bottom‐up approach. Additionally, the correlation between readers' perceived use of strategies and their reading scores was statistically significant.

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)0.024
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.491
GPT teacher head0.581
Teacher spread0.090 · 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