<b>THE POWER OF BABEL: TEACHING AND LEARNING IN MULTILINGUALCLASSROOMS.</b><i>Viv Edwards</i>. Stoke-on-Trent, UK: Trentham Books, 1998.Pp. 88. £11.95 paper.
Bibliographic record
Abstract
As a result of large-scale migrations from the less developed to the more developed countries, the multilingual, multicultural school is becoming a reality, not only in cities with traditionally large immigrant populations such as New York, Los Angeles, and Toronto, but throughout North America and Western Europe. Indeed, the 1990 U.S. Census shows that one in every seven children between the ages of 5 and 17 comes from a home where a language other than English is spoken. These major demographic changes have left many teachers and other school personnel unprepared to deal with the new realities of the multilingual, multicultural classroom. Edwards' brief volume is directed to mainstream classroom teachers who, if current trends continue as expected, will spend much of their careers in even more linguistically and culturally diverse schools than exist currently. Edwards' intent is to counter widespread myths about second language learning and bilingualism, and to assist teachers maximizing the rich educational resource that the multicultural, multilingual school represents.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.194 | 0.073 |
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".