Relative difficulty in the L2 acquisition of the Spanish dorsal fricative
Bibliographic record
Abstract
Research on relative difficulty in L2 production has revealed that learners target the most salient parameter when acquiring new sounds (Colantoni & Steele, 2008). For example, L1 English-L2 French learners acquire the more salient fricative manner of the French /ʁ/ before the voicing and duration parameters (Colantoni & Steele, 2007, 2008). Previous work in this framework has not compared the acquisition of place and manner parameters. If the more salient parameter is targeted first, we should expect L2 learners to acquire the manner of articulation before the place of articulation, given that manner is a more salient feature than place (Miller & Nicely, 1955; Bedoin et al., 2013). This hypothesis was tested by investigating the L2 production of the Spanish voiceless dorsal fricative by L1 English speakers living in Madrid, a region in which the fricative has a strident realization (Hualde, 2014) and a uvular place of articulation (Ibabe et al., 2016). Fourteen L1 English-L2 Spanish speakers and 14 native Spanish controls performed a picture description task that elicited the target in two vocalic contexts: [aχe, eχa]. An acoustic analysis revealed that the L2 speakers produced fricatives with a similar amplitude compared to controls. However, in the [eχa] context, the learners produced fricatives with a more anterior place of articulation and less frication. The results are consistent with the finding from previous work that learners focus on the most salient property when learning new segments, and provide further evidence that vocalic context is an important factor in production difficulty.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".