Language Learning in the Wild: The L2 Acquisition of English Restrictive Relative Clauses
Bibliographic record
Abstract
We argue that quantitative analysis of community-based speech data furnishes an indispensable adjunct to theoretical and experimental studies targeting the acquisition of relativization. Drawing on a comparative sociolinguistic approach, we make use of three corpora of natural speech to investigate second-language (L2) speakers’ acquisition of restrictive relative clauses in English. These corpora comprise: (i) spontaneous L2 speech; (ii) a local baseline variety of the target language (TL); and (iii) L2 speakers’ first language (L1), French. These complementary datasets enable us to explore the extent to which L2 speakers reproduce the discursive frequency of relative markers, as well as their fine-grained linguistic conditioning, in the local TL baseline variety. Comparisons with French facilitate exploration of possible L1 transfer effects on L2 speakers’ production of English restrictive relative clauses. Results indicate that evidence of L1 transfer effects on L2 speakers’ restrictive relative clauses is tenuous. A pivotal finding is that L2 speakers, in the aggregate, closely approximate TL constraints on relative marker selection, although they use the subject relativizer who significantly less often than their TL counterparts. We implicate affiliation with, and integration into, the local TL community as key factors facilitating the propagation of TL vernacular norms to L2 speakers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".