Improving Education for Migrant-Background Students: A Transatlantic Comparison of School Funding.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it