The impact of national integration policies on prejudice and psychological well‐being: The fundamental role of the clarity and coherence of integration policies
Bibliographic record
Abstract
Abstract Recent advances in social psychological research have shown that national integration policies influence how immigrants are perceived and treated by the mainstream population. However, the processes by which these policies come to have an impact on prejudice and well‐being of the general population are largely unknown. Moreover, past research has often relied on unrepresentative samples limiting generalizability. The present research proposes that when the national integration policies of a given society are perceived as clearly defined and coherent, they have a direct impact on the perceived cultural norm related to those national integration policies. In turn, the perceived cultural norm affects personal attitudes toward cultural norms (i.e., the degree to which individuals support a given policy), which influence the level of anti‐immigrant prejudice as well as psychological well‐being. Findings from two correlational studies with representative samples of the mainstream population conducted in France on the policy of secularism ( N = 940) and in Québec on the policy of interculturalism ( N = 912) supported the proposed theoretical model.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| 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".