Restrictions on definiteness insecond language acquisition
Bibliographic record
Abstract
In this paper we investigate whether learners of L2 English show knowledge of the Definiteness Effect ( Milsark, 1977 ), which restricts definite expressions from appearing in the existential there-insertion construction. There are crosslinguistic differences in how restrictions on definiteness play out. In English, definite expressions may not occur in either affirmative or negative existentials (e.g. There is a/*the mouse in my soup; There isn’t a/*the mouse in my soup). In Turkish and Russian, affirmative existentials observe a restriction similar to English, whereas negative existentials do not. We report on a series of experiments conducted with learners of English whose L1s are Turkish and Russian, of intermediate and advanced proficiency. Native speakers also took the test in English, Turkish, and Russian. The task involved acceptability judgments. Subjects were presented with short contexts, each followed by a sentence to be judged as natural/unnatural. Test items included affirmative and negative existentials, as well as items testing apparent exceptions to definiteness restrictions. Results show that both intermediate and advanced L2ers respond like English native speakers, crucially rejecting definites in negative existentials. A comparison with the groups taking the test in Russian and Turkish confirms that judgments in the L2 are quite different from the L1, suggesting that transfer cannot provide the explanation for learner success.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".