Do Positive and Negative Intergroup Contact Create Shifts in Ingroup and Outgroup Attitudes Over Time: A Three‐Wave Longitudinal Study Testing Alternative Mediation Models
Bibliographic record
Abstract
ABSTRACT The current study investigated how contact experiences may be associated with attitudes towards the ingroup and the outgroup using a three‐wave longitudinal study. We assessed Turkish native children's contact with Syrian refugees (N = 487, Mage = 10.60, SDage = 0.90) and explored relationships between initial contact and later ingroup and outgroup attitudes testing alternative mediation models. We also examined whether negative contact with outgroup members may directly or indirectly predict more positive ingroup attitudes. Findings demonstrated that positive contact was associated with both reduced ingroup positivity and increased outgroup positivity over time. However, unlike the traditionally suggested mediational pathway in contact‐deprovincialization literature, initial positive contact (T1) was associated with less positive ingroup attitudes (T3) through more positive outgroup attitudes at T2. There was no evidence for the role of negative intergroup contact on ingroup or outgroup attitudes. Findings are discussed within the broader scope of contact theory and the recently growing deprovincialization literature. 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".