Language ideology and the native speaker ideal : Canadian and Norwegian attitudes toward ESL/EFL pedagogical models
Bibliographic record
Abstract
Previous sociolinguistic studies done in Norway have explored attitudes toward native speaker and Norwegian accented varieties of English. This study adds a new angle by comparing the attitudes of first language speakers of English from Canada and second language speakers of English from Norway toward SC (Standard Canadian) and NE (Norwegian accented English) accents. An online survey was undertaken by 107 English teachers, of which 50 self-identified as Norwegian and 57 as Canadian teachers of English. Respondents evaluated 3 matched-guise audio clips consisting of one SC accent, one light NE accent and one heavy NE accent. Norwegians evaluated the SC accent more positively than Canadians in 3 out of 5 categories and both NE accents more negatively than Canadians in 9 out of 10 categories; further they were considerably more negative toward the heavy NE accent than the light NE accent. A possible explanation of this contrast stems from the inability of Canadians to recognize Norwegian accents, as 65 % of Canadians interpreted both the heavy and light NE accents as examples of native speaker English accents. The findings suggest that attitude judgements from outside parties toward NE accents may be directed by the ability to recognize the provenance of the accents. In-depth interviews of 3 Norwegian and 3 Canadian English teachers strengthened the findings by revealing feelings of "correctness" toward native speaker accents in the Norwegian group and a more ambivalent, communication based attitude in the Canadian group. Norwegian respondents felt that acquiring native-like accents led to confident language teachers and students. Implications of this study contribute to an understanding of ESL teaching in Canada and ELF/EFL teaching in Norway.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".