Bibliographic record
Abstract
Will immigration undermine the welfare state? Trust beyond Borders draws on public opinion data and case studies of Germany, Sweden, and the United States to document the influence of immigration and diversity on trust, reciprocity, and public support for welfare programs. Markus M. L. Crepaz demonstrates that we are, at least in some cases, capable of trusting beyond borders: of expressing faith in our fellow humans and extending help without regard for political classifications. In Europe, the welfare state developed under conditions of relative homogeneity that fostered high levels of trust among citizens, while in America anxiety about immigration and diversity predated the emergence of a social safety net. Looking at our new era of global migration, Crepaz traces the renewed debate about "us" versus "them" on both sides of the Atlantic and asks how it will affect the public commitment to social welfare. Drawing on the literatures on immigration, identity, social trust, and the welfare state, Trust beyond Borders presents a novel analysis of immigration's challenge to the welfare state and a persuasive exploration of the policies that may yet preserve it. "Crepaz contributes much to our knowledge about the link between immigration and social welfare, certainly one of the central issues in current national and international politics." ---Stuart Soroka, Associate Professor of Political Science and William Dawson Scholar, McGill University "Finally! A book that challenges the growing view that ethnic diversity is the enemy of social solidarity. It addresses an issue of intense debate in Western nations; it takes dead aim at the theoretical issues at the center of the controversy; it deploys an impressive array of empirical evidence; and its conclusions represent a powerful corrective to the current drift of opinion. Trust beyond Borders will rank among the very best books in the field." ---Keith Banting, Queen's Research Chair in Public Policy, Queen's University "Do mass immigration and ethnic diversity threaten popular support for the welfare state? Trust beyond Borders answers no. Marshaling an impressive array of comparative opinion data, Crepaz shows that countries with high levels of social trust and universal welfare state arrangements can avoid the development of the welfare chauvinism that typically accompanies diversity." ---Gary Freeman, Professor and Department Chair, Department of Government, University of Texas at Austin Markus M. L. Crepaz is Professor in the Department of International Affairs at the University of Georgia and Associate Director of the Center for the Study of Global Issues (GLOBIS).
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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".