Impact of the intrinsic complexity and prior linguistic knowledge on the acquisition of relative clauses
Bibliographic record
Abstract
The study explores the extent to which the intrinsic complexity of relative clauses (RCs) and prior linguistic knowledge impact the acquisition of RCs by L2 learners. The study investigates the main sources of the erroneous and avoided types of English RCs produced by Persian-speaking learners of English at three proficiency levels. The data elicitation task was a translation test comprised of six types of RCs modeled on the RC types in the Noun Phrase Accessibility Hierarchy. To analyze the data, the occurrence frequencies of the correctly and erroneously formed RCs were counted and the avoided RCs were identified in each RC type. Then, a precise error analysis was done. The statistical analysis of 3840 RCs showed that the most common error types were (i) forming English RCs with resumptive pronouns and (ii) altering more-marked RCs with non-canonical word order to less-marked RCs with canonical word order. The errors are interpreted as evidence for the impact of both L1 transfer and the universal intrinsic constraints of RCs. The analysis of the avoided RC types, mostly the more marked RCs, indicates that avoidance is mainly linked to the universal intrinsic constraints of RCs.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".