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Word Classes in Second Language Acquisition

2023· book-chapter· en· W4389925783 on OpenAlexaff
Seth Lindstromberg, Frank Boers

Bibliographic record

VenueOxford University Press eBooks · 2023
Typebook-chapter
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsLearnabilityAdjectiveLinguisticsAge of AcquisitionConcretenessWord (group theory)NounVerbPart of speechClass (philosophy)PsychologyComputer scienceNatural language processingArtificial intelligenceCognitive psychologyCognition

Abstract

fetched live from OpenAlex

Abstract With respect to post-childhood acquisition of an additional language (L2), it has sometimes been suggested that word class is an important co-determinant of ‘word learnability’, defined as the speed and thoroughness with which a once unknown L2 word can be integrated into a learner’s lexicon. This chapter reviews evidence for this suggestion, largely with respect to the open word classes noun, verb, and adjective, since these are the classes that have received the most attention in empirical research. It begins by discussing variables other than word class which empirical researchers have identified as notable co-determinants of word learnability; for example, semantic variables such as concreteness (i.e. the perceptibility of the referent) and degree of polysemy, form variables such as length, the usage-based variable item frequency, and interlingual or partly interlingual variables such as cognateness and pronounceability. Then, with respect to the difficulty of isolating an effect of word class on word learning, the chapter gives examples of how experimental studies which seemed to have revealed a word-class effect are in fact at least as likely to have indicated an effect of some other variable. There seems to be no compelling evidence for an independent word-class effect on word learnability.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.000

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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueOxford University Press eBooksSame topicSecond Language Acquisition and LearningFrench-language works237,207