Intervention in relative clauses: Effects of relativized minimality on L2 representation and processing
Bibliographic record
Abstract
This article reports on an experiment investigating the effects of featural Relativized Minimality (Friedmann et al., 2009) on the representation and processing of relative clauses in the second language (L2) English of Mandarin speakers. Object relatives (ORCs) are known to cause greater problems in first language (L1) acquisition and in adult processing than subject relatives (SRCs). Featural Relativized Minimality explains this in terms of intervention effects, caused by a DP (the subject of the ORC) located between the relative head and its source. Intervention effects are claimed to be reduced if the relative head and the intervenor differ in features, such as number (e.g. I know the king who the boys pushed). We hypothesize that L2 learners will show intervention effects when processing ORCs and that such effects will be reduced if the intervenor differs in number from the relative head. There were two tasks: picture identification and self-paced reading. Both manipulated relative clause type (SRC/ORC) and intervenor type (±plural). Accuracy was high in interpreting relative clauses, suggesting no representational problem. Regarding reading times, ORCs were processed slower than SRCs, supporting an intervention effect. However, faster reading times were found in ORCs when intervenor and head noun matched in number, contrary to hypothesis. We suggest that our more stringent stimuli may have resulted in the lack of an effect for mismatched ORCs, in contrast to some earlier findings for L1 acquirers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".