Stereotypes and the discursive accomplishment of intergroup differentiation
Bibliographic record
Abstract
This article analyzes how employees in a global business organization talk about their colleagues in other countries. Employees were asked to discuss their work practices in focus group settings, and give examples of how they experience ‘the other’. Using Discursive Psychology and Politeness Theory as the analytic approaches, the article analyzes pieces of discourse to disclose social psychological phenomena such as group identity, intergroup differentiation, and stereotypes. The analyses show that talking about ‘the other’ is potentially face-threatening, and mitigating discourse features are used repeatedly to soften the criticism. We also see how uncovering stereotypes is a mutual accomplishment in the group, and how group members gradually move from relatively innocent to blatantly negative outgroup stereotypes. The analyses also show that participants engage in meta-reflections on the nature of stereotypes, which may serve as another mitigating device, and that talk about ‘the other’ is used to create intergroup differentiation. Finally, the article discusses the implications of these findings for cross-cultural communication and work practices in organizations.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".