Bibliographic record
Abstract
This paper discusses the first language acquisition of the Direct Object Clitic Doubling (henceforth [DOCD]) parameter in Romanian, a [+DOCD] language, and reports the results of a pilot experiment that identifies the default setting of this parameter. The hypothesis tested here assumes, following Sportiche (1996, 1997), that [-DOCD] is the default value of this parameter, while the [+DOCD] value is acquired on the basis of positive evidence. Following the Full Competence Hypothesis (FCH) as proposed by Poeppel & Wexler (1993) among others, I assume that functional categories (i.e. clitics) are present in the child’s grammar from the beginning. The working hypothesis in this paper predicts that in earlier stages of language acquisition children will entertain the [-DOCD] parameter, even when they are learning a [+DOCD] language. These predictions are borne out by the results of my study. This pilot experiment was designed to investigate the process of acquisition of the [+DOCD] value; in this respect, children at different stages of acquisition and adult control participants were tested. Two tasks focusing on the DOCD values were used: an elicited production task and an imitation task. We conclude that in the process of L1 acquisition of [DOCD] the default value is [-DOCD]. Key words: Romanian L1 acquisition, direct object clitics acquisition, clitic doubling acquisition, parameteric settings, functional categories.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".