Book Review: Making People Illegal: What Globalization Means For Migration and Law, by Catherine Dauvergne
Bibliographic record
Abstract
IN THE TWENTIETH CENTURY, many of the leading jurisprudential debates swirled around the question of whether morally heinous and procedurally flawed Nazi dictates were deserving of the label "law."In Making People Illegal, Catherine Dauvergne makes the fairly broad claim that immigration law may well be the equivalent site for debates about the rule of law in the twenty-first century.3 Dauvergne develops her argument that immigration law can teach us a great deal about the rule of law by using an adapted version of "the ice scientist's methodology of core sampling."' Rather than offer a comprehensive review of immigration law in jurisdictions around the world, Dauvergne selects a small number of problematic areas of "illegal" migration,' and examines how the rule of law and immigration issues intersect in these areas.Dauvergne begins her analysis by making an empirical claim that is central to the rest of her argument: human rights norms, she tells us, have been largely
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".