Policy Preferences on Immigration and Evaluations of Individual Immigrants. A Cross-National Experiment
Bibliographic record
Abstract
The survey experiment is part of a cross national project on Evaluations of Immigrants and Policy Preferences. The Norwegian Survey is funded by the Research Council of Norway and directed by Toril Aalberg. Shanto Iyengar at Stanford University is Principal Investigator and director of a similar US survey, while Ray Duch conducted the survey experiment in the United Kingdom. Stuart Soroka directed a Canadian version while Kees Aarts have been responsible for a Dutch version of this study. The main purpose of the Norwegian survey experiment was to investigate Norwegians’ attitudes towards individual immigrants and to assess the consistency between policy preferences on the one hand, and willingness to admit individual immigrants on the other. It was equally important to examine whether the same factors that influence policy opinions also affect how people evaluate individual immigrants. Therefore the survey included batteries of questions that had been used in traditional cross-national surveys on immigration. Additionally it was also included questions about specific groups of immigrants and how the respondents evaluated two specific individual immigrants. Following the more traditional survey questions respondents were presented with two vignettes each describing a potential male immigrant. In these vignettes the information that was given about the immigrants’ socio-economic, cultural and ethnical background was manipulated.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".