The attitudes of Chinese people towards fluent Chinese second language speakers of English
Bibliographic record
Abstract
Previous research has demonstrated that a relationship exists between perceived accent and ethnic group loyalty in a conflictual situation (i.e., when the members of an ethnic group are in conflict with members of the target language group) and that such a relationship may have behavioral consequences. The present study investigated (1) whether such a relationship exists in a situation when there is no conflict between the two language groups involved (i.e., for native Chinese speakers learning English in Montreal), (2) what the behavioral manifestations of this relationship would be, and (3) what factors influenced this relationship. Eighty-four participants from mainland China residing in Montreal listened to native Chinese reading a passage in English (spoken with various degrees of foreign accent) and Chinese in a matched-guise procedure. They then judged the speakers' accentedness, loyalty towards the Chinese, personality traits and ability to be leaders and members of two different group situations. Results revealed that a relationship between perceived accent and ethnic group loyalty indeed exists in a non-conflictual situation and that this relationship has consequences upon native Chinese listeners' choices of speakers as leaders and members of their group. These consequences are different from those observed in earlier research in a conflictual situation. Results overall highlight the importance of group factors in L2 learning and suggest the need to consider ethnic group loyalty as a variable in both applied linguistic research and L2 pedagogy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".