Preparing a Prosperous Future: Promoting Culture and Business Through Bilingual Education
Bibliographic record
Abstract
This paper describes an ambitious educational program uniting the efforts of Swiss, German, and French business associations, corporations, government agencies, and regional school boards in the Upper Rhine Valley, where economy and culture transcend national borders. The objectives of the program are to promote bilingualism, to teach the young people what factors unite and what factors differentiate their communities, and to optimize conditions for prosperous economic development based on knowledge of geographic, historical, sociocultural, and socioeconomic data. The focus of this paper is the program's textbook, Leben am Oberrhein/ Vivre dans le Rhin Supérieur [Living in the Upper Rhine Valley],1 created especially for the communities of the Upper Rhine Valley and the center of the program. The program is multifaceted and innovative. This paper concentrates on Europe (where the program originated) as well as the United States of America (home to the author and the majority of Bilingual Research Journal readers) and its neighboring countries (essentially Canada and Mexico). Yet the program can serve as an inspiring model throughout the world for community leaders and educators wishing to promote bilingualism within communities formed by native speakers of a dominant language and groups of various heritage language speakers and cultures, or in neighboring communities with different backgrounds and mother tongues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".