Light verbs and the flexible use of words as noun and verb in early language learning
Bibliographic record
Abstract
L'auteur a accord une licence non exclusive permettant la Bibliothque nationaledu Canada de reproduire, prter, distribuer ou vendre des copies de cette thse sous la forme de microfiche/film, de reproduction sur papier ou sur format lectronique.L'auteur conserve la proprit du droit d'auteurquiprotgeoette thse.Ni la thse ni des extraits substantiels de celle.-cine doivent tre imprims Ou autrementreproduits sans son autorisation. 0-612-78824-5Canada i eombination with those for Sarah, Eve, and Naomi.AIso, analyses extended weIl beyond data for deverbal nouns, and eonsidered aIl words that eould be used flexibly as noun and verb without phonologieal change.Finally, the actual methods of analysis used in this thesis differed importantly from those used by Oshima-Takane, Barner, Eisabbagh, & Guerriero (1999; 2001), and involved detailed statistieai analyses of how target words wereused in various noun-verb proportions, how words were used in eomplex predieate eonstructions,how nouns as a class were used to denote various semantie categories, and how ehildren produeed light verbs in early acquisition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".