On Intolerance and Immigration: Understanding Perceptions \nof Intra- and Extradiversity in Denmark and Canada
Bibliographic record
Abstract
The increasing pace of immigration to the Western world and the subsequent xenophobic backlashes to immigrants has created an urgent need for empirical research that examines the dynamics of immigration and xenophobia. This project addresses that dynamic through a comparative analysis of Denmark and Canada, whose histories since World War II have shaped both official responses and dominant discourses in ways that position the two countries at near opposite ends of the spectrum of immigration responses in the Western world. \nMoving away from linear, macro-level models employed in most immigration research, this project employs methods triangulation. It uses both qualitative and quantitative data to explore the hypothesis that the perceived level of diversity – of the 'self' and the 'other' – is instrumental in shaping the dynamics within which discourses and attitudes about immigration are negotiated. \nThe research findings support the diversity hypothesis while also causing us to expand on it: not only is the receiving population's negotiation of the national identity vis a vis diversity central in shaping responses to immigration, but the nature of the distinction between the 'self' and the 'other' is instrumental in this negotiation process. Furthermore, the level of society from which the identity negotiation process stems - whether group-based or focused on the individual - plays a large role in shaping the responses to immigration.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".