The English Pronunciation Teaching in Europe Survey: Factors inside and outside the Classroom
Bibliographic record
Abstract
In the past two decades, a number of studies have looked at how English pronunciation is taught, focusing on teaching practices, materials, training and attitudes to native speaker models from both the teachers’ and the learners’ perspective. Most of these studies have been conducted in English-speaking countries such as the USA (Murphy, 1997), Great Britain (Bradford and Kenworthy, 1991; Burgess and Spencer, 2000), Canada (Breitkreutz, Derwing and Rossiter, 2001; Foote, Holtby and Derwing, 2011), Ireland (Murphy, 2011) and Australia (Couper, 2011; Macdonald, 2002). In Europe, pronunciation teaching has been studied in Spain (Walker, 1999) and, more recently, in Finland (Tergujeff, 2012, 2013a, b). Work has also looked at attitudes towards native speaker models and the degree of success in reaching the model, for example, in Poland (Nowacka, 2010; Waniek-Klimczak, 2002;Waniek-Klimczak and Klimczak, 2005), Serbia (Paunović, 2009) and Bulgaria (Dimitrova and Chernogorova, 2012). In Finland, Lintunen (2004) and Tergujeff, Ullakonoja and Dufva (2011) focused on learners, not teachers, but both studies included a survey section exploring methods in English pronunciation teaching. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".