Bibliographic record
Abstract
The challenges and opportunities associated with the education of immigrants predate modern school systems, though it certainly can be said that support for public schooling grew—for example, in Canada and the United States—as dominant (read White, Anglo-Saxon, Protestant) groups came to see the importance of integrating masses of disparate origin. Educational responses to the children of immigrants over time have been varied, and many responses are indistinguishable from efforts to address other minority groups. In North America, the rapid expansion of immigration encompassing immigrant and refugee populations from around the world, particularly since the 1960s, has led to a number of structural and curricular changes in schools, only some of which were explicitly aimed at immigrants. One example of a policy aimed at the children of immigrants was bilingual education, and as this increasingly fell out of favor, ESL (English as a second language) classrooms became more common. However, with few exceptions, neither has been a very effective instrument for addressing the interests or concerns of immigrants themselves. Nevertheless, most parents strongly prefer that their children learn to master the dominant language, as a means of getting ahead.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".