Liberal Nationalism and Its Critics : Normative and Empirical Questions
Bibliographic record
Abstract
"The thesis of liberal nationalism is that national identities can serve as a source of unity in culturally diverse liberal societies, thereby lending support to democracy and social justice. The chapters in this book examine that thesis from both normative and empirical perspectives, in the latter case using survey data or psychological experiments from the U.S., Canada, the Netherlands, Denmark, France, and the UK. They explore how people understand what it means to belong to their nation, and show that different aspects of national attachment--national identity, national pride, and national chauvinism--have contrasting effects on support for redistribution and on attitudes towards immigrants. The psychological mechanisms that may explain why people's identity matters for their willingness to extend support to others are examined in depth. Equally important is how the potential recipients of such support are perceived. 'Ethnic' and 'civic' conceptions of national identity are often contrasted, but the empirical basis for such a distinction is shown to be weak. In their place, a cultural conception of national identity is explored, and defended against the charge that it is 'essentialist' and therefore exclusive of minorities. Particular attention is given to the role that religion can legitimately play within such identities. Finally the book examines the challenges involved in integrating immigrants, dual nationals, and other minorities into the national community. It shows that although these groups mostly share the liberal values of the majority, their full inclusion depends on whether they are seen as committed and trustworthy members of the national 'we'"--
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.090 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".