perception and comprehension of L2 English sentence types
Bibliographic record
Abstract
L2 prosody is particularly difficult to acquire, because it requires an understanding of intonation, syntax, and pragmatics. For example, to acquire English sentence types, speakers must learn that statements (Ss) and absolute yes/no questions (AQs) are syntactically and prosodically marked, whereas the difference between Ss and declarative questions (DQs) is purely prosodic. Moreover, DQs can only occur in restricted contexts, such as to express surprise. In this paper, we examine the L2 perception and comprehension of English sentence types, by speakers of three typologically distinct L1s (Spanish, Mandarin, Inuktitut), with the goal of investigating the role of crosslinguistic influence (CLI). Spanish uses only intonation (a higher initial pitch accent and final rising boundary tone) to distinguish Ss from AQs and DQs, whereas in Mandarin, questions (AQs and DQs) can be syntactically identical to statements or marked by the lexical particle –ma. Mandarin also has a prosodic distinction between broad focus and echo questions (which are similar to English AQs and DQs). In contrast, Inuktitut has a very restricted use of pitch, and primarily marks questions morphologically. Learners of each L1 and English controls performed three tasks that varied in the amount of contextual and linguistic information available. Our results revealed evidence of both positive and negative CLI. Inuktitut and Mandarin speakers demonstrated some tendencies to focus more on syntax than intonation. Moreover, the Mandarin speakers were the most successful at acquiring the pragmatic distinction between AQs and DQs, which we argue is due to a similar contrast in Mandarin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".