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Morphological Theory and Second Language Acquisition

2018· reference-entry· en· W2951833528 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

Venuenot available
Typereference-entry
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsBrock UniversityUniversity of Victoria
Fundersnot available
KeywordsLinguisticsComputer scienceNatural language processingArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Most of the world’s population speaks a second language. Most of the words in a language are multimorphemic. Thus, the study of second language acquisition and processing offers a key window onto the nature of morphological representation and morphological ability. This chapter examines the acquisition, representation, and processing of inflected, derived, and compound words in a second language. Perspectives on second language variability and accuracy are surveyed, as well as perspectives on the path of development of morphological knowledge in second language learners. The chapter highlights how linguistic theory and experimentation can be used to (a) advance the understanding of morphological representation and processing, and (b) probe what underlies morphological ability. Finally, this chapter addresses the question of how an integrated bilingual mental lexicon can both maximize interlingual connectivity and accommodate the morphological differences that exist between languages.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.405
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.4080.002

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 head0.017
GPT teacher head0.311
Teacher spread0.294 · 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

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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