Exploring social attitudes toward second language speakers of English across Canada
Bibliographic record
Abstract
Nearly a fifth of Canada’s population is represented by people from other countries, most of whom speak languages other than English or French. Though there has been some exploration of social attitudes toward these ethnolinguistic groups, there have been no multi-city investigations of social attitudes, particularly toward second language (L2) speakers, in major cities where immigrant populations are most concentrated. Furthermore, there have been few studies that present speech samples alongside images suggesting speaker ethnicity and/or religious affiliation. Therefore, this dissertation explores ratings of L2 speakers across multiple dimensions and considers the role of social attitudes and social network exposure in formation of those judgments. \n \nStudy 1 explored how residents of Calgary and Montreal judge the comprehensibility and accentedness of L2 speech in audio-only and audiovisual conditions and whether those judgments are associated with residents’ overall social attitudes toward immigrants. There were no context or image effects, but differences emerged among ratings of certain language groups, as well as raters’ general attitudes toward immigrants. Ultimately, raters’ attitudes were not associated with their ratings of L2 speech. \n \nStudy 2 explored how native-born residents of Canada judge L2 speakers’ intelligence, friendliness, and trustworthiness and investigated how those judgments might be related to residents’ general social attitudes toward immigrants. No significant differences emerged between speech samples presented as audio-only versus with a nonreligious or religious image. However, there was a clear hierarchy in how specific language groups were evaluated. Social attitudes questionnaire responses revealed generally positive attitudes toward immigrants. Ultimately, those attitudes had weak relationships with rater judgments of L2 speakers’ intelligence, friendliness, and trustworthiness. \n \nStudy 3 explored how native-born residents of Montreal and Calgary compared in judgments of L2 speaker citizenship and how those judgments might be related to raters’ L2 social networks. No differences based on image condition or context surfaced in judgments of L2 speaker citizenship. However, specific language groups differed in how their citizenship status was perceived. There were also between-context differences in native-born residents’ interactions with L2 speakers. Ultimately, social network characteristics had no influence on citizenship ratings in either 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".