The development of preverbs in Northern East Cree: A longitudinal case study
Bibliographic record
Abstract
This study is based on naturalistic speech samples produced by one child learning Cree as her first language (2;01-4;03) and presents the first investigation into the development of preverbs in the language. Preverbs are an optional class of morpheme which precede the lexical verb stem, dividing into grammatical, lexical and directional (deictic) subclasses. Of nine preverb types in the child's inventory, 47/48 tokens are grammatical. We argue that these appear early because they exhibit phonological transparency (do not alternate in form) and positional predictability (are restricted to preverb position). Lexical and directional preverbs, however, alternate in form and may appear in either preverb position or within the lexical stem. Furthermore, the child first began to use preverbs with the grammatically simpler independent (default) inflection (2;04), and 7 months later with the more syntactically restrictive conjunct inflection (2;11). She also used each preverb with one inflection type only, even where a choice of inflections was available, and she never produced more than one preverb per verb complex although multiple preverbs are common. In sum, grammatical complexity appears to be a driving factor in determining the child's development pattern.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".