The effects of contact on native language pronunciation in an L2 migrant setting
Bibliographic record
Abstract
The primary aim of this study was to determine whether native speakers of German living in either Canada or the Netherlands are perceived to have a foreign accent in their native German speech. German monolingual listeners (n = 19) assessed global foreign accent of 34 L1 German speakers in Anglophone Canada, 23 L1 German speakers in the Dutch Netherlands, and five German monolingual controls in Germany. The experimental subjects had moved to either Canada or the Netherlands at an average age of 27 years and had resided in their country of choice for an average of 37 years. The results revealed that the German listeners were more likely to perceive a global foreign accent in the German speech of the consecutive bilinguals in Anglophone Canada and the Dutch Netherlands than in the speech of the control group and that nine immigrants to Canada and five immigrants to the Netherlands were clearly perceived to be non-native speakers of German. Further analysis revealed that quality and quantity of contact with the native German language had a more significant effect on predicting global foreign accent in native speech than age of arrival or length of residence. Two types of contact were differentiated: (i) C−M represented communicative settings in which little code-mixing between the L1 and L2 was expected to occur, and (ii) C+M represented communicative settings in which code-mixing was expected to be more likely. The variable C−M had a significant impact on predicting foreign accent in native speech, whereas the variable C+M did not. The results suggest that contact with the L1 through communicative settings in which code-mixing is inhibited is especially conducive to maintaining the stability of native language pronunciation in consecutive bilinguals living in a migrant context.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".