Bibliographic record
Abstract
Contrary to mid-twentieth century predictions, ethnic pluralism has increased dramatically in North America and significantly in Europe. Neither the post 9/11 emphasis on international border security nor anti-immigration and anti-multiculturalism movements have affected the fifty year trend of increasing labour mobility and sustained levels of migration. The ethnic pluralism accompanying this powerful trend has fueled academic research and public debate. Contributors report on and develop a conceptualization of ethnic social incorporation and multiculturalism in Canada, the United States, Germany, Greece, Bulgaria and Italy. This group of countries displays a remarkable variety of both ethnic diversity and public policy responses to ethnic social incorporation over the past four decades. It includes two countries (Canada and the United States) built upon very large-scale immigration over the course of more than a century, two countries (Greece and Italy) which until recently were characterized by large-scale emigration but now are grappling with immigration, one country (Bulgaria) that was until the 1990s insulated from extensive migration and faces a demographic slump, and one (Germany) that has experimented with isolating temporary populations but is now addressing the responsibilities of permanent immigration. Multicultural Variations includes national reports describing each of the six countries under investigation and is book-ended by introductory and concluding chapters that present a new understanding of and synthesis on multiculturalism that is distinct from either enthusiastic support or ideological critiques. Contributors include Mathias Bös (Philipps-Universität Marburg; Germany), Antonio Chiesi, (Università degli Studi di Milano, Italy), Jason Edgerton (University of Manitoba, Canada), Barry Ferguson (University of Manitoba, Canada), Nikolai Genov (Freie Universität Berlin, Germany), Louis Hicks (St Mary's College of Maryland, USA), Paul Kingston (University of Virginia, USA), Laura Maratou-Alipranti (National Centre for Social Research, Athens, Greece), Lance W. Roberts (University of Manitoba, Canada), Sonia Stefanizzi (Università degli Studi di Milano-Bicocca, Italy), and Susanne von Below (Johann Wolfgang Goethe- Universität Frankfurt, Germany),
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".