Bilingual education and bilingualism
Bibliographic record
Abstract
03–526 Bialystok, Ellen (York U., Canada; Email: ellenb@yorku.ca ) Majumder, Shilpi and Martin, Michelle M. Developing phonological awareness: Is there a bilingual advantage?Applied Psycholinguistics (Cambridge, UK), 24, (2003), 27–44. 03–527 Blas Arroyo, José Luis (Jaume I U., Castellon, Spain; Email: blas@fil.uji.es ). The languages of the Valencian educational system: the results of two decades of language policy. International Journal of Bilingual Education and Bilingualism (Clevedon, UK), 5, 6 (2002), 318–338. 03–528 De Mejía Anne-Marie and Tejada, Harvey (U. del Valle, Cali, Columbia; Email: atruscot@mafalda.univalle.edu.co ). Bilingual curriculum construction and empowerment in Columbia. International Journal of Bilingual Education and Bilingualism (Clevedon, UK), 6, 1 (2003), 37–51. 03–529 Jia, Gisela (Lehman Coll., City U. of New York; Email: giselaj@lehman.cuny.edu ) and Aaronson, Doris. A longitudinal study of Chinese children and adolescents learning English in the United States. Applied Psycholinguistics (Cambridge, UK), 24 (2003), 131–161. 03–530 Keim, Inken (Institut für Deutsche Sprache, Mannheim, Germany). Sprachvariation und sozialer Stil am Beispiel jugendlicher Migrantinnen türkischer Herkunft in Mannheim. [Language variation and communicative style among young female immigrants of Turkish origin in Mannheim.] Deutsche Sprache, 30, 2 (2002), 97–123. 03–531 Valdés, Guadalupe and Angelelli, Claudia. Interpreters, interpreting, and the study of bilingualism. Annual Review of Applied Linguistics (Cambridge, UK), 23 (2003), 58–78.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".