Institute for Research on Poverty Discussion Paper no. 1371-09 Income Poverty and Income Support for Minority and Immigrant Children in Rich Countries
Bibliographic record
Abstract
Action Fund for support. We also thank Brian Murphy for his help in preparing the Canadian data for this manuscript, and the University of Oxford for access to the EU-SILC data that underlie this publication. We thank Deborah Johnson, Emma Caspar, and Dawn Duren for help with manuscript preparation. We thank Brian Nolan, Marta Tienda, Sara McLanahan, Don Hernandez, and seminar participants at the Princeton Seminar “Migrant Youth and Children of Migrants in a Globalized World ” for helpful suggestions. Finally, the authors thank the Luxembourg Income Study member countries, especially, for their support. The conclusions reached are those of the authors alone and not of their sponsoring institutions. IRP Publications (discussion papers, special reports, Fast Focus, and the newsletter Focus) are available on the Internet. The IRP Web site can be accessed at the following address:
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.136 | 0.029 |
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".