“Your English is Good for an Immigrant”: Examining Mixed Effects of Mindset Messages on Perceived Linguistic Potential of and Blame Attributions Towards ESL Migrants
Bibliographic record
Abstract
Social exclusion can exacerbate newcomers’ language difficulties and undermine their social integration. We examined whether language mindsets induce mixed attitudes towards migrants with limited proficiency in the target language, and indirectly affect willingness to interact with migrants and attitudes toward migrants’ language education. Across two pre-registered experiments ( N = 531) conducted in Canada, we found that people who were primed with fixed (vs. growth or control) mindsets tended to believe migrants have less potential to improve their English, but were less likely to blame them for their lack of improvement (“not their fault if they can't improve”), suggesting fixed mindsets contribute to mixed attitudes toward migrants. Furthermore, perceived linguistic potential was negatively and blame was positively correlated with contact avoidance and opposition to publicly funded language education for newcomers. These effects held after controlling for political orientations and perceived fluency of the target speaker, suggesting that language mindsets contribute to language judgments that could impact migrants’ acculturation experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".