The Role of Task Complexity and Dominant Articulatory Routines in the Acquisition of L3 Spanish
Bibliographic record
Abstract
Many studies in L3 phonetics and phonology have found that language dominance plays an influential role in determining the source of transfer. However, any effect of language dominance is likely dependent on many factors, including task complexity. As complexity increases, learners should be increasingly likely to rely on the more automatic articulatory routines from their dominant language. We tested this hypothesis by examining the production patterns of L1 Mandarin–L2 English–L3 Spanish speakers acquiring the Spanish tap and trill, performing a less complex word-reading task and a more complex sentence reading task. The results of the former were reported in a previous study, revealing that the speakers transferred the L2 English [ɹ] and [ɾ] to some extent when acquiring the Spanish rhotics. We hypothesized that such transfer would be less prevalent in the same speakers performing the sentence reading task. The results revealed some support for the hypothesis. Transfer of L2 [ɾ] decreased in the sentence reading task, as did transfer of L2 [ɾ] (in trill productions). L2 [ɹ] substitutes did not vary with task. The results highlight that transfer from previous languages is partially dependent on task. Future work should establish when and to what extent language dominance influences the source of transfer.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".