Improving Education for Migrant-Background Students: A Transatlantic Comparison of School Funding.
Bibliographic record
Abstract
The educational needs of migrant-background students in primary and secondary schools pose a growing challenge for policymakers and educators around the world. Some national, regional, and local governments have well-designed systems of support for such students, while others are just beginning to establish targeted policies and practices to meet the needs of this growing and diverse population. For policymakers, school funding designs are an important means of influencing how schools and school districts serve their students who are immigrants or the children of immigrants. The rules guiding such funding design usually both reflect and drive the larger goals and priorities of the education system. By providing supplementary funding for high-need groups, such as migrant-background students, policymakers signal that helping these students access the services they need to succeed on par with their peers is a priority. This report focuses on four countries--Canada, France, Germany, and the United States--shedding light on supplementary funding mechanisms targeted to migrant-background students, and some of the key challenges and strategies decisionmakers are wrestling with as they attempt to ensure that additional resources are used effectively.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".