Jolanta Szpyra-Kozłowska: Pronunciation in EFL Instruction: A Research-Based Approach. Second Language Acquisition Series: 82.Bristol– Buffalo – Toronto: Multilingual Matters, 2014
Bibliographic record
Abstract
English is nowadays indisputably a truly global language, and international communication in English for the most part takes place among non-native speakers, who outnumber native speakers.This has had an important impact on pronunciation theory, research and teaching practice, as native models of English pronunciation have ceased to be unchallenged ideals.Accordingly, some of the most hotly debated issues in English pronunciation teaching theory and practice include the appropriate pronunciation model(s) and the phonetic teaching agenda for learners of English who are assumed future users of English as a Lingua Franca (ELF) -a newly emerging mode of communication among speakers of different first language backgrounds.It is precisely the discussion of these issues that Jolanta Szpyra-Koz owska, a professor of English phonetics and phonology at Maria Curie--Sk odowska University, Lublin and a prolific scholarly writer in the related fields, sets out to contribute to and does so very insightfully in the book under review.Based on extensive empirical research on the pronunciation features of specific first-language groups of learners of English as a Foreign Language (EFL), in this 249-page study the author offers her insights about the currently most controversial issues.The book is organized into three main chapters, which are introduced by a Preface and followed by Concluding Remarks, References, Author Index, and Subject Index.The three main chapters have the same structure: each of them consists of two parts, A and B, which are further subdivided into three to eight sections.In Chapter 1, English Pronunciation Teaching: Global Versus Local Contexts, the author actually expresses the main point of the book.She stresses the importance of the institutionalized teaching and learning of English pronunciation based on expert knowledge and using some of the major native models of pronunciation, which have
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".