Bibliographic record
Abstract
Biolinguistics sees language as a cognitive organ and L1-acquisition as a process of language growth. A natural assumption within this approach is that of Lenneberg (1967), who assumes that language growth is subject to certain time restrictions. Some scholars hold Lenneberg’s assumption to be correct; pointing out that L2-acquisition differs from L1-acquisition in not being uniform, automatic or convergent as the latter is; a difference that could follow from loss to access to the mental mechanisms responsible for L1-acquisition due to aging. Many researchers, however, refute Lenneberg’s assumption, pointing out that foreign languages are natural languages and must therefore be constrained by UG; the very mechanism responsible for L1 acquisition. I argue that this debate has taken place without a working model of the design of language. I show that, without such a model, the questions of the debate are misleading. I further show that once a minimalist model is considered, a time restriction on language growth is consistent with the fact that foreign languages are UG constrained. Finally, I argue that time restrictions only constrain those areas of language that involve parameter-setting (e.g. lexical learning), and never those determined by language design.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".