Bibliographic record
Abstract
The syntactic system of human language consists of different levels of units such as clauses, phrases, and grammatical categories. Grammatical categories are part of the system in all syntactic models as these units are the building blocks for larger syntactic units. Phrases and sentences are defined in terms of grammatical categories (rather than individual words) so that an infinite number of utterances can be represented. Children must acquire grammatical categories in order to develop a complete syntactic system. Grammatical categories have therefore received continuous focus in language acquisition research (e.g. Bloom 1970; Brown, 1973; Radford, 1990). One key question has been how children break into the system of syntactic categories. In this chapter I will discuss several models addressing this question. I will then focus on our model which suggests that speech input contains sufficient acoustical and phonological cues to support the division of words into two initial broad categories – content words and function words – and that these two categories serve as the entry point to the syntactic system for the learner. I will present our empirical work on input speech as well as on learners' processing of these two categories. I will argue that acquisition of this initial distinction plays an important role not only for syntactic acquisition, but also for other aspects of language development including word segmentation and the initial mapping of word meaning.
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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 | 0.000 | 0.001 |
| 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 | 0.005 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".