Bilingual language acquisition and theories of diachronic change: Bilingualism as cause and effect of grammatical change
Bibliographic record
Abstract
Children acquiring their first languages are frequently regarded as the principal agents of diachronic change. The causes and the precise nature of the processes of change are, however, far from clear. The following discussion focuses on possible changes of core properties of grammars which, in terms of the theory of Universal Grammar, can be characterized as reflecting different settings of parameters. In such cases, learners develop grammatical competences differing from those of speakers of the previous generation who provided the primary data serving as input for the developmental processes. It has been argued that reanalyses of this type must be conceived of as instances of transmission failure. Yet acquisition research has demonstrated that the human Language Making Capacity is extraordinarily robust, thus leading to the question of what might cause unsuccessful acquisition. Changing frequencies in use or exposure to data containing ambiguous or even contradictory evidence are unlikely to suffice as causes for this to happen. Language acquisition in multilingual settings may be a more plausible source of grammatical reanalysis than monolingual first language development. The study of contemporary bilingualism can therefore contribute to an explanation of diachronic change. Yet one such insight is that simultaneous acquisition of two languages (2L1) typically leads to a kind of grammatical knowledge in each language which is qualitatively not different from that of the respective monolinguals, obliging us to look for other sources of transmission failure. 2L1 acquisition in settings where one language is “weaker” than the other has been claimed to qualify as such. But I will argue that even such problematic cases do not provide convincing evidence of reanalysis. If, on the other hand, children receive sustained input from second language learners, or if their onset of acquisition is delayed, this can indeed lead to incomplete acquisition. I conclude that successive acquisition of bilingualism plays a crucial role as a source of grammatical change. In order for such changes to happen, however, grammar-internal and language-external factors may have to concur.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".