Bibliographic record
Abstract
Abstract Bilingual settings are perceived as exemplary cases of linguistic diversity, and they are assumed to trigger cross-linguistic interaction. The rationale underlying this assumption is the belief that when more than one language is processed in a brain, this will inevitably affect the way in which linguistic knowledge is acquired, stored and used. However, this idea stands in conflict with results obtained by research on children acquiring two (or more) languages simultaneously. They have been demonstrated to be able to differentiate languages from early on and to develop competences qualitatively identical to those of monolinguals. These studies thus provide little evidence supporting the idea that bilingualism must lead to divergent grammatical development. The question then is what triggers alterations of bilinguals’ grammars, especially of the syntactic core, possibly resulting in non-native competences. This has been claimed to occur in the acquisition of second languages, weaker languages of simultaneous bilinguals, or heritage languages. These acquisition types differ from first language development in that onset of acquisition of one language is delayed or that the amount of exposure to one language is reduced. I will argue that age at onset and severely reduced amount of exposure are potential causal factors triggering divergent developments, whereas bilingualism on its own is not a sufficient cause of divergence.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".