Bibliographic record
Abstract
This paper investigates object omission in French longitudinal production from two English-speaking children (Lightbown, 1977). Similar patterns of object omission are observed: direct objects start being dropped as transitive verbs are emerging and licit and illicit null objects occur in all recordings thereafter. Moreover, the incidence of illicit null objects drops at about the same time in both children (month 20), which corresponds to the moment object clitics start being used productively and to the end of the root infinitive period (Prévost, 1997). I argue that object omission is an instance of clitic-drop and is related to processing difficulties. In particular, both the projection of full-fledged representations and the production of object clitics (which occupy non-canonical object positions in French) increase computational complexity for children. Object-drop in child L2 French does not seem to be affected by L1 transfer nor to be related to significant difficulties with properties at the syntax/pragmatics interface.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".