Editorial: L2 Phonology Meets L2 Pronunciation
Bibliographic record
Abstract
Topic L2 Phonology Meets L2 PronunciationThe theme of this collection is "L2 Phonology Meets L2 Pronunciation."Such an interdisciplinary approach, of course, runs the risk of any gathering of friends at which you discover that your chessclub friends having nothing to say to your ultimate-frisbee teammates.We are sure, however, that these papers reveal this not to be the case here.In various guises as researchers and teachers, the three editors have tackled the question of what is easy and what is difficult in both learning and teaching (the sub-theme of the collection).We hope you find our introductory mini-reviews helpful in setting the stage.One is on pronunciation teaching (O'Brien), one on functional load (Sewell), and one on L2 phonology (Archibald).There are clear similarities in what L2 phonologists are interested in, and what L2 pronunciation teachers are interested in.All three of these themes are intertwined in the collection.What we have assembled here are papers written by people who have been fascinated by these same questions.In these nine papers, there are some which focus on consonants (Cardoso et al.; Stefanich and Cabrelli; Zhang and Levis), some on vowels (Cebrian et al.; Munro), some on prosody (Ghosh and Levis; Liu and Reed), and some on teaching (Colantoni et al.; Kostromitina and Kang).We group them in this way to reflect the Commentaries by eminent scholars that appear after the papers.We thank Shea, Thomson, McGregor, and Sonsaat Hegelheimer for accepting our invitation to round out the Research Topic.Of course, many of the papers reveal that the boundary line between phonology and pronunciation is really quite blurry.Such is the reality of scholarly life.Stefanich and Cabrelli look at the production of the Spanish alveopalatal nasal/ɲ/by L1 English speakers.They illustrate the complex developmental path of acquisition of this new sound.Zhang and Levis look at a less-studied consonantal pattern: the effects of a merger of/n/and/l/in the Southwestern Mandarin dialect of Chinese.They demonstrate that this L1 property affects production in Standard Mandarin differently than it affects English production.Cardoso et al. look at the effects of different types of instruction on the acquisition of consonantal sequences.They show that the group which received instruction on the most marked structure fares the best.Munro presents a fascinating data set of Cantonese learners of English tense/lax vowels, and shows that there is a great deal of individual and lexical variation which makes it very challenging to talk of a monolithic notion of difficulty.Cebrian et al. probe the relationship between perceived similarity judgments of English tense/lax vowels by Spanish/Catalan native speakers and their perception and production.They discover that perceived similarity is not always a good predictor of discrimination ability.Colantoni et al. draw on the pronunciation literature, and set out and illustrate some design principles for enhancing L2 Spanish intelligibility in the classroom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".