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Record W2056052115 · doi:10.1075/eurosla.1.09lig

Input filters in second language acquisition

2001· article· en· W2056052115 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

VenueEUROSLA Yearbook · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsConcordia University
Fundersnot available
KeywordsCognitionVariety (cybernetics)InterlanguageComputer sciencePsychologyCognitive psychologySecond-language acquisitionLinguisticsSpeech recognitionArtificial intelligence

Abstract

fetched live from OpenAlex

This paper reviews a variety of restrictions (input filters) on the conversion of input to intake and thence to acquisition. These filters are internal characteristics of the learner which seem to interfere with the ability to make use of L2 input for acquisition, even when that input seems, on the surface, to be appropriate and plentiful. Three sorts of filters are examined: affective filters, auditory/phonological filters, and cognitive filters. In the third category, three kinds of cognitive filters are discussed: (a) overload or conflict in the processing systems, (b) developmental filters, and (c) effects of previously learned languages. The discussion focuses on the role of instruction and feedback in making input more accessible to classroom learners and guiding them to perceive the difference between interlanguage patterns and those of the target language.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.991

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)0.0090.001

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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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