The new African diaspora in North America : trends, community building, and adaption
Bibliographic record
Abstract
Chapter 1 An Overview of African Immigration to the U.S. and Canada Chapter 2 Theoretical Perspectives on African Migration Chapter 3 The Contradictions of Globalization: Causes of Contemporary African Immigration to the United States of America Chapter 4 Location and Settlement Patterns of African Immigrants in the U.S.: Demographics and Spatial Context Chapter 5 African Immigrants in Canada: Trends, Socio-demographic and Spatial Aspects Chapter 6 Creating the Vertical Village: Senegalese Traditions of Immigration and Transnational Cultural Life Chapter 7 Ethnic Identity Conflict and Diasporic Constructions in the New World:The Case of Asante in North America Chapter 8 Still Praisin' God in a New Land: African Immigrant Christianity in NorthAmerica Chapter 9 Nigerians in North America: New Frontiers, Old Associations? Chapter 10 Africans in the Diaspora: Black-White Earnings Differences Among America's Africans Chapter 11 Unintended Consequences: Liberalized U.S. Immigration Law and the African Brain Drain Chapter 12 Knocking at the Door: Professional African Immigrant and Refugee Women's Experiences: in the Toronto Labor Market Chapter 13 Lifting the Yoke of Tradition: New African Market-Women Diaspora: From Kaneshi, Accra to Harlem, New York Chapter 14 English Language Acquisition and Some Pedagogical Issues Affecting the Adaptation of African Immigrant Children Chapter 15 Transnationalism Among African Immigrants in North America: The Case Ghanaians in Canada Chapter 16 Conclusions and Policy Recommendations
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".