Languages in America: A pluralist view. 2nd edn. By Susan Dicker. (Bilingual education and bilingualism 42.) London: Multilingual Matters, 2003. Pp. 363. ISBN 1853596515. $29.95.
Bibliographic record
Abstract
Reviewed by: Languages in America: A pluralist viewby Susan Dicker I. M. Laversuch Languages in America: A pluralist view. 2nd edn. By Susan Dicker. (Bilingual education and bilingualism 42.) London: Multilingual Matters, 2003. Pp. 363. ISBN 1853596515. $29.95. As the ethnolinguistic composition of the United States continues to shift, ideological arguments over [End Page 195]the proper direction of the nation continue. In this well-balanced, thought-provoking book, Susan Dicker provides a valuable introduction to the many ways that public and private attitudes towards cultural pluralism in the United States have combined to form official language policy and unofficial language behavior. Towards this end, Ch. 1 gives readers a solid introduction to the general topic of culture and identity. Ch. 2 follows this discussion with a clear presentation of the early ethnolinguistic plurality in the United States resulting from the progressive immigration of various cultural groups. In addition to standard discussion of typical European immigrant groups such as the Germans, French, and Hispanics, this chapter also provides important background information about groups that have received relatively little attention in similar studies. In particular, much information is provided on Chinese immigrants to the US. By comparison, surprisingly little attention is given to two nonimmigrant groups that form important minority groups in the US—African Americans and Native Americans. Aside from this shortcoming, this volume is no less than outstanding. Indeed, the very next chapter (3) on popular misconceptions about language learning is an excellent example. With clarity and sensitivity, D confronts many of the popular stereotypes held about language learning and demonstrates the ways in which they betray a basic misunderstanding of the complex processes involved. D, with clear and relevant examples, then goes on to systematically present solid, up-to-date research with the important proviso that future research may necessitate revision or amendment. As the author points out, the past tendency of some researchers to present their findings as if they were laws is in no small measure responsible for the skepticism and public resistance to new, improved language policies. Perhaps nowhere is this fallout more evident than in the schizophrenic language policies of the American school system. Accordingly, Ch. 4 shows how recently introduced changes to the national education system have cheated native English-speaking students by blocking the instruction of foreign languages and deceived nonnative English-speaking students by promoting the erasure of ethno-linguistic identity. Further echoes of fossilized misconceptions about language and learning are revealed in Ch. 5, which is devoted to exposing many of the faulty arguments used by supporters of ‘English only’ movements in the US. Ch. 6 then details various challenges to restricting language policy in several US states (e.g. Hawaii, Louisiana, and Texas). This presentation is then placed within a larger international context in Chs. 7 and 8, which give information about how other nations like Canada, India, and Switzerland have dealt with issues related to ethnolinguistic diversity. Taken all together, this book brilliantly reveals how highly publicized arguments over US language policies are often poorly camouflaged ideological battles; and like most battles, the highest number of casualties are usually the most innocent victims of all, our children. I. M. Laversuch University of Zurich Copyright © 2006 Linguistic Society of America
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.032 |
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".