PROTECT: The impacts of the UN's Global Compacts on Refugees and Migration on the citizens’ recognition of the right to international protection
Bibliographic record
Abstract
The University of Bergen, as the lead partner of work package 6 of PROTECT, devised and conducted this survey with fieldwork contribution from the SYNO-CINT-Faktum Consortium of survey firms. The data set aims to measure the attitudes of citizens in 26 countries to different aspects and components of international refugee protection. In addition, it provides some potential explanatory variables measured at the individual level, ranging from regular demographic variables (e.g., age, gender, country, region, income) to variables measuring respondents’ position within the global political cleavage system, political orientations, visions about the global political order, notions of citizenship, attitudes to diversity, media use, trust in institutions, etc. Data were collected in the following countries: Austria, Belgium, Canada, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Hungary, Italy, Lithuania, Mexico, Netherlands, Norway, Poland, Romania, Slovakia, Slovenia, South Africa, Spain, Sweden, Turkey , UK and USA. The data was collected in full compliance with the General Data Protection Regulation (Regulation (EU) 2016/679) after informed consent from the respondents. Licensed under Creative Commons Attribution 4.0 International.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".