Bibliographic record
Abstract
Amanda Kibler writes that "as a result of an increasingly mobile global population, minority and majority language issues in education are prominent worldwide."(p.31) Kibler's particular interest is the English language education of non-native speakers.She presents case studies concerning two primary schools-one in England, and the other in the state of Texas-where pupils' first languages are used to help them become more proficient in English. Analyses of relevant documents, interviews, as well as classroom observations"illuminate how teachers and schools serving highly diverse linguistic and ethnic populations function within broader language policy directives."(p.7)In her brief introduction (Chapter One), Kibler writes regarding both England and the United States: "While policy rhetoric may support the notion that a pupil's first language is a linguistic and cultmal resource, literacy in this language is valued principally as a vehicle for learning English."(p.7) However, as Kibler points out, case study research makes it possible to see beyond such generalizations.We learn, for example, about how certain staff members regard students' first languages as much more than just a means to another linguistic end.In Chapter Two, Kibler discusses "Language Planning and Policy in Education."She draws attention to the English-education-only policies in Arizona, California and Massachusetts, as well as other policies which result in "discrimination against speakers of minority languages."(p.14) Kibler also considers second language acquisition research which suggests that bilingual education is beneficial for pupils who are learning English.Incidentally, the Association for Supervision and Curriculum Development has an online "ResearchBrief' about "The Effects of Bilingual Education Programs on English Language Learners."(March 2, 2004) Chapter Three is a detailed presentation of "Language-in-Education Policies in England and the United States."In England pupils learning English are referred to as 'bilingual' or 'English as an additional language' [EAL); whereas in the United States, the term is 'limited English proficient' (LEP) (p.21).In 1999, EAL students represented 8.1% of England's compulsory school population.(Ibid.)In the United States, in 2000-01, LEP students made up 9.8% of the compulsory school population (p.26).In this chapter, Kibler talks about literacy, curriculum, assessment, teacher training, and funding as they relate to EAL or LEP students.She also briefly discusses the history of bilingual education in England and the U.S. and looks at some specific policies concerning first-language support in the classroom.In Chapter Four, Kibler sets out her methodology and explains her comparative perspective.She bases her approach partly on George Bereday's
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.423 | 0.376 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".