Expressing Prejudice through the Linguistic Intergroup Bias: Second Language Confidence and Identity among Minority Group Members
Bibliographic record
Abstract
The role of verbal communication in the transmission of prejudice has received much theoretical attention (Hecht, 1998; Le Couteur & Augoustinos, 2001), including the features of the linguistic intergroup bias (Maass, Salvi, Arcuri, & Semin, 1989), yet few studies have examined the acquisition of an out-group language as a factor in mitigating prejudicial speech. The conditions under which minority Canadian Francophones use linguistic bias when communicating about the in- and out-group (i.e., Canadian Anglophones) were investigated. Data was collected from 110 Francophone students. Predictions were confirmed but only when out-group identification was considered. Further, out-group identification and second language confidence were both related to a decrease in out-group derogation; however, the same factors appear to promote linguistically biased speech toward the in-group. Results are discussed within current intergroup communication theory.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".