Bibliographic record
Abstract
04–107Amara, Muhammad Hasan (Bar-Ilan University). Recent foreign language education policies in Palestine. Language Problems and Language Planning (Amsterdam, The Netherlands), 27, 3 (2003), 217–232. 04–108Bekker, Ian.Using historical data to explain language attitudes: A South African case study.Africa and Applied Linguistics: AILA Review (Amsterdam, The Netherlands), 16 (2003), 62–77. 04–109Costa, Albert, Colomé Angel, Gómez, Olga and Sebastián-Gallés, Núria (U. of Barcelona, Spain; Email: Acosta@psi.ub.edu ). Another look at cross-language competition in bilingual speech production: lexical and phonological factors. Bilingualism: Language and Cognition (Cambridge, UK), 6, 3 (2003), 167–179. 04–110Dei, G. and Asgharzadeh, A. (University of Toronto, Canada; Email: gdei@oise.utoronto.ca ). Language, education and development: case studies from the southern contexts. Language and Education (Clevedon, UK), 17, 6 (2003), 421–449. 04–111Ferguson, Gibson.Classroom code-switching in post-colonial contexts: functions, attitudes and policies. Africa and Applied Linguistics: AILA Review (Amsterdam, The Netherlands), 16 (2003), 38–51. 04–112Jackson, Jane (Chinese U. of Hong Kong; Email: jjackson@cuhk.edu.hk ). Case-based learning and reticence in a bilingual context: perceptions of business students in Hong Kong. System (Oxford, UK), 31 (2003), 457–469. 04–113Kouega, J-P. (Email: jkouega@uycdc.uninet.cm ). English in francophone elementary grades in Cameroon. Language and Education (Clevedon, UK), 17, 6 (2003), 408–420. 04–114Ovando, Carlos, J. (Arizona State U., USA). Bilingual education in the United States: historical development and current issues. Bilingual Research Journal (Arizona, USA), 27, 1 (2003), 1–24.
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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.130 | 0.037 |
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".