Exploring Secondary Transfer Generalisation Effects From Black and Gay Contact: The Role of Humanisation
Bibliographic record
Abstract
ABSTRACT Intergroup contact is considered one of the most effective ways to reduce prejudice. An extension of contact theory, the secondary transfer effect (STE), stipulates that contact with a primary outgroup can impact attitudes toward a second, uninvolved outgroup. Here, we test the direct and indirect effects of contact with the primary outgroup on attitudes toward the secondary outgroup through outgroup humanisation, assessing White, heterosexual Americans' contact with both Black and gay people ( N = 471; 52.7% men; M age = 44.90, SD = 14.75). Path analyses were conducted on four fully saturated models that included intergroup contact (quantity, quality), humanisation of each group, and intergroup outcomes (attitudes, collective action intentions). Direct generalisation consistently occurred from gay contact (quantity or quality) to Black attitudes or Black collective action. Only one indirect generalisation pathway consistently occurred: a greater quantity of gay contact humanised Black people, which itself was associated with more positive attitudes and stronger collective action intentions toward Black people. However, the converse generalisation was not found: Black contact was rarely associated with direct or indirect intergroup outcomes toward gay people. The present study is the first to find indirect humanisation pathways for the STE, but from gay‐to‐Black contact only. Implications for future research are discussed. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement .
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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".