Book Review: Distant Stage: Quebec, Brazil, and the Making of Canada’s Cultural Diplomacy
Bibliographic record
Abstract
Although the book focuses on the period between 2001 and 2021 to illustrate how containing diversity works, it also shows how Canada's colonial roots still impact its immigration policy.For this reason, Abu-Laban, Tungohan, and Gabriel make a significant contribution to the contemporary literature on immigration in Canada.Echoing Abu-Laban and Gabriel's earlier arguments in Selling Diversity, 3 this latest book illustrates the paradox between Canada's mechanisms of exclusion and its efforts to embrace diversity through liberal discourse on openness, multiculturalism policies, and public support for immigration.In this sense, Canada is far from being an "exception" in the twenty-first century immigration landscape.The use of an ethics-of-care perspective combined with critical political economy opens new avenues to rethink immigration in Canada, instead of seeing migrants as numbers or quantifiable objects that would contribute to certain neo-liberal objectives.One of the most exciting contributions to the immigration literature in the last few years, Containing Diversity is a valuable resource not only for migration scholars, but also for policy analysts, as well as immigrants themselves who wish to learn about Canadian immigration policies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".