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Record W3192586460 · doi:10.3389/fcomm.2021.639398

On the Difficulty of Defining “Difficult” in Second-Language Vowel Acquisition

2021· article· en· W3192586460 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueFrontiers in Communication · 2021
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVowelPronunciationLinguisticsPsychologyRhymeIntelligibility (philosophy)PhonologySchema (genetic algorithms)Computer science

Abstract

fetched live from OpenAlex

Hierarchies of difficulty in second-language (L2) phonology have long played a role in the postulation and evaluation of learning models. In L2 pronunciation teaching, hierarchies are assumed to be helpful in the development of instructional strategies based on anticipated areas of difficulty. This investigation addressed the practicality of defining a pedagogically useful hierarchy of difficulty for English tense and lax close vowels (/i I u ʊ/) produced by Cantonese speakers. Unlike their English counterparts, Cantonese close tense-lax pairs are allophonic variants with [i u] occurring before alveolars and [I ʊ] before velars. Each tense-lax pair represents a “phonemic split” in which members of a single L1 category are realized contrastively in L2. Despite evidence that English tense-lax distinctions are challenging for Cantonese speakers, no previous empirical work has closely considered the problem from the standpoint of vowel intelligibility across multiple phonetic contexts and in different words sharing the same rhyme. In a picture-based word-elicitation task, 18 Cantonese-speaking participants produced 31 high-frequency CV and CVC words. Vowels were evaluated for intelligibility by phonetically-trained judges. A series of mixed-effects binary logistic models were fitted to the scores, with vowel quality, phonetic context (rhyme) and word as factors, and length of Canadian residence and daily use of English as co-variates. As expected, the general hierarchy of difficulty for vowels that emerged (/i/ > /u/ > /ʊ/ > /I/) was complicated by large differences across phonetic contexts. Results were not readily explicable in terms of transfer; moreover, different words with the same rhyme were not produced with equal intelligibility. The most serious modeling complication was the sizeable inter-speaker variability in difficulties, which could not be accounted for by model co-variates. Although some difficulties were roughly systematic at the group level, it is argued that establishing a pedagogically useful hierarchy on such data would prove intractable. Rather, L2 learners might be better served by assessment and instructional targeting of their individual problem areas than by a focus on errors predicted from hierarchies of difficulty.

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)0.001
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 score0.786
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.019
GPT teacher head0.317
Teacher spread0.297 · 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