Bibliographic record
Abstract
Canada’s history, since its birth as a nation one hundred and fifty years ago, is one of immigration, nation-building, and contested racial and ethnic relations. In Immigration, Racial and Ethnic Studies in 150 Years of Canada: Retrospects and Prospects scholars provide a wide-ranging overview of this history with a core theme being one of enduring racial and ethnic conflict and inequality. The volume is organized around four themes where in each theme selected racial and ethnic issues are examined critically. Part 1 focuses on the history of Canadian immigration and nation-building while Part 2 looks at situating contemporary Canada in terms of the debates in the literature on ethnicity and race. Part 3 revisits specific racial and ethnic studies in Canada and finally in Part 4 a state-of-the-art is provided on immigration and racial and ethnic studies while providing prospects for the future. Contributors are: Victor Armony, David Este, Augie Fleras, Peter R. Grant, Shibao Guo, Abdolmohammad Kazemipur, Anne-Marie Livingstone, Adina Madularea, Ayesha Mian Akram, Nilum Panesar, Yolande Pottie-Sherman, Paul Pritchard, Howard Ramos, Daniel W. Robertson, Vic Satzewich, Morton Weinfeld, Rima Wilkes, Lori Wilkinson, Elke Winter, Nelson Wiseman, Lloyd Wong, and Henry Yu.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".