Immigrant geographies of North American cities
Bibliographic record
Abstract
PREFACE ACKNOWLEDGEMENTS CONTRIBUTORS INTRODUCTION: IMMIGRANT GEOGRAPHIES: ISSUES AND DEBATES AUDREY KOBAYASHI, WEI LI, AND CARLOS TEIXEIRA PART I: THE INTERNATIONALIZATION OF NORTH AMERICAN CITIES AND SUBURBS 1. Going Local: Canadian and American Immigration Policy in the New Century HELGA LEITNER AND VALERIE PRESTON 2. Immigration Trends in the United States and Canada: A Historical Perspective DIRK HOERDER AND SCOTT WALKER PART II: THE IMPRINT OF IMMIGRATION IN NORTH AMERICAN CITIES AND SUBURBS 3. Immigration and Urban and Suburban Settlements ROBERT A. MURDIE AND EMILY SKOP 4. The Spatial Segregation and Socio-economic Inequality of Immigrant Groups JOE DARDEN AND ERIC FONG 5. Immigrants, Refugees, and Housing THOMAS CARTER AND DOMENIC VITIELLO 6. Economic Experiences of Immigrants LUCIA LO AND WEI LI 7. How Gender Matters to Immigration and Settlement in Canadian and US Cities DAMARIS ROSE AND BRIAN RAY 8. Immigration, Health, and Health Care LU WANG, ELIZABETH CHACKO, AND LINDSAY WITHERS 9. Immigrant Political Incorporation in American and Canadian Cities ELS DE GRAAUW AND CAROLINE ANDREW PART III: IMMIGRANT GROUPS IN NORTH AMERICAN CITIES AND SUBURBS 10. Contemporary Asian Immigrants in the United States and Canada SHUGUANG WANG AND QINGFANG WANG 11. Contemporary Profiles of Black Immigrants in the United States and Canada THOMAS BOSWELL AND BRIAN RAY 12. Latin American Immigrants: Parallel and Diverging Geographies LUISA VERONIS AND HEATHER SMITH 13. Crossing the 49th Parallel: American Immigrants in Canada and Canadians in the US SUSAN HARDWICK AND HEATHER SMITH CONCLUSION: A REVIEW AND SOME SIGNIFICANT FINDINGS JAMES ALLEN AND CARLOS TEIXEIRA GLOSSARY INDEX
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".