<scp>Stephen J. Caldas</scp>, <i>Raising bilingual-biliterate children in monolingual cultures</i>
Bibliographic record
Abstract
Stephen J. Caldas, Raising bilingual-biliterate children in monolingual cultures. Clevedon, UK: Multilingual Matters, 2006. Pp. xvi, 231. Pb. $39.95. This is the fascinating story of the author's own family project of raising his three children French-English bilingually in English-speaking Louisiana. Caldas, a French-English bilingual himself, and his bilingual French-Canadian wife artificially orchestrate and manipulate the children's environments from birth to adolescence to ensure that the children develop full bilingual proficiency and biliteracy in French and English. Caldas's and his wife's main strategy is to speak only French to their son and their identical twin daughters. They also commit to use only French with each other, thus creating an all-French-speaking home environment. The Caldases also enroll the children in French immersion school and make extensive use of French-language media to further expose the children to French. Finally, the author and his wife purchase a cottage in Quebec where they spend the summers, providing the children with authentic societal language immersion. The outcome of this extraordinary experiment is that, by adolescence, all three children are completely bilingual and biliterate in French and English and can be easily mistaken as native speakers of both Quebecois French and American English.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.124 | 0.058 |
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".