Bibliographic record
Abstract
Parents often ask me for advice about exposing their children to two languages in the home. Typically, one of the parents speaks some language other than English and they are concerned that their linguistic decisions will have consequences for the child's development. The requests come in many forms (although e-mail has become the channel of choice) and from people with obviously different levels of background knowledge, education, and experience. The motivation for their questions is usually the same – will the child learn English and will the experience of learning two languages lead to either cognitive or linguistic confusion? These questions are interesting because of the assumptions they reveal about the folk wisdom of childhood bilingualism. First, people intuitively believe that language learning is a fragile enterprise and can be easily disrupted. Second, they assume that languages interact, and that learning one language has implications for learning another. Finally, they expect that what happens with language can impact on the rest of cognition. All of these assumptions are empirical questions and all of them entail theoretical controversies. Moreover, they are questions for which controlled investigation is difficult, if not intractable. Ironically, it is bilingual children who also provide the most promising forum for their examination and a means of potentially resolving the theoretical disputes. What happens to children's developing knowledge of language if they are learning two languages at the same time? How do children sort out the words and meanings from the two systems and incorporate them into thought?
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.455 | 0.290 |
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".