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Record W4242346824 · doi:10.1017/cbo9781139087636

Second Language Speech

2015· book· en· W4242346824 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceScripting languagePhoneticsLinguisticsFocus (optics)Second-language acquisitionComprehension approachField (mathematics)PsychologyNatural languageArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

Second language acquisition has rapidly grown as a field over the past decade, as our knowledge of the ways in which children and adults learn and use a second language has become crucial for effective language teaching. In addition to this important 'applied' function, research into second language acquisition has also informed the fields of linguistics and psychology in general, as it has shed light on the differences between native and non-native models of human language and cognition. The focus of this accessible new book is second language speech - that is, how speakers perceive, process, understand and pronounce the sounds of a second language. Each chapter includes review questions, and most chapters include 'tutorial' and 'lab' sections with practical exercises based on the University of Toronto Romance Phonetics Database (available online for free). The book also has a companion website, containing illustrated answers to the exercises, scripts for running acoustic analyses and useful weblinks.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.413
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 integrity0.0000.001
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 head0.032
GPT teacher head0.209
Teacher spread0.177 · 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