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Record W3178144966 · doi:10.1017/s0272263121000279

QUANTIFYING THE DIFFERENCE IN READING FLUENCY BETWEEN L1 AND L2 READERS OF ENGLISH

2021· article· en· W3178144966 on OpenAlex
Kelly Nisbet, Raymond Bertram, Charlotte Erlinghagen, Aleks Pieczykolan, Victor Kuperman

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueStudies in Second Language Acquisition · 2021
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGermanFluencySpellingLinguisticsReading (process)VocabularyPsychologyComponent (thermodynamics)DisadvantagedSet (abstract data type)Language proficiencyComputer scienceMathematics education

Abstract

fetched live from OpenAlex

Abstract This study is a comparative examination of reading behavior of first-language (L1) Canadian and second-language (L2) Finnish and German readers of English. We measured eye-movement patterns during reading the same set of English sentences and administered tests of English vocabulary, spelling, and exposure to print. The core of our study is a novel method of statistical prediction used to generate hypothetical Finnish and German participants with maximum observed L1 scores in all component skills. We found that with L1-like component skills, hypothetical German readers can show the same reading speed as the L1 group. We hypothesize this advantage comes from the small linguistic distance to English. Conversely, hypothetical Finnish readers remain disadvantaged even with maximum component skills, likely due to a larger linguistic distance. We discuss theoretical and applied implications of our method for L2 acquisition research.

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)0.000
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 score0.466
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.052
GPT teacher head0.365
Teacher spread0.313 · 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