Bibliographic record
Abstract
International comparisons are not always as valued as they need to be in the world of policy-makers, practitioners, and researchers.Too often, opportunities for engaging one's peers on questions of mutual concern are deemed to be junkets that should quickly be jettisoned in difficult economic times.While this common sense approach is certainly common, it is not necessarily sensible.In the world of social policy, experiments are rare and fraught with ethical challenges; thus, a reliance on examining what others have attempted and what they learned from the experience becomes indispensible.The annual International Metropolis Conferences have been a space for these global conversations on integration and migration for the past 15 years.Without them and many of the core figures that have animated them, this volume would not have been possible.We owe a debt of gratitude to Meyer Burstein, Rinus Penninx, Jan Rath, Paul Spoonley, and Erin Tolley, to name but a few.In Canada we are indebted to Rachelle Leroux and Jodi Peterson of the Citizenship and Immigration Canada library, who helped us locate references in the wide and eclectic universe of
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.394 | 0.283 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".