When words become borders: Ingroup favoritism in perceptions and mental representations of Anglo-Canadian and Franco-Canadian faces
Bibliographic record
Abstract
Language is critical to social identity, including nationality. However, some nations encompass multiple languages, raising questions about how their citizens perceive members of their national versus linguistic groups. We explored perceptions of Canadian nationality, which consists of two linguistic groups: Anglo-Canadians and Franco-Canadians. In Study 1, we used reverse correlation methods to visualize how Anglo- and Franco-Canadians mentally represent the faces of linguistic ingroup and outgroup members, and of Canadians in general. Structural similarity analyses and subjective ratings of the resulting images showed that both groups mentally represented Canadians as more similar to their own linguistic ingroup. In Study 2, Anglo-Canadians and Franco-Canadians rated photos of real Anglo- and Franco-Canadian targets. Both samples showed some ingroup favoritism when inferring their traits but only Anglo-Canadians could accurately differentiate group members. Differences between Anglo-Canadians and Franco-Canadians therefore extend beyond language, with linguistic groups impacting impressions before any words are spoken.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".