Humanitarianism, identity, and nation : migration laws of Australia and Canada
Bibliographic record
Abstract
Refugees are on the move around the globe. Prosperous nations are rapidly adjusting their laws to crack down on the so-called “undeserving.” Australia and Canada have each sought international reputations as humanitarian do-gooders, especially in the area of refugee admissions. Humanitarianism, Identity, and Nation traces the connections between the nation-building tradition of immigration and the challenge of admitting people who do not reflect the national interest of the twenty-first century. Catherine Dauvergne argues that in the absence of the justice standard for admitting newcomers, liberal nations instead share a humanitarian consensus about letting in needy outsiders. This consensus constrains and shapes migration law and policy. In a detailed consideration of how refugees and others in need are admitted to Australia and Canada, she links humanitarianism and national identity to explain the current shape of the law. If the problems of immigration policy were all about economics, future directions would be easy to map. If rights could trump sovereignty, refugee admission would be straightforward. But migration politics has never been simple. Humanitarianism, Identity, and Nation is a welcome antidote to economic critiques of immigration, and a thoughtful contribution to rights talk. It is a must-read for everyone interested in transforming migration laws to meet the needs of the twenty-first century. [From UBC Press | Humanitarianism, Identity, and Nation - Migration Laws in Canada and Australia, By Catherine Dauvergne]
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.045 | 0.014 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".