Perception and Production of Sentence Types by Inuktitut-English Bilinguals
Bibliographic record
Abstract
We explore the perception and production of English statements, absolute yes-no questions, and declarative questions by Inuktitut-English sequential bilinguals. Inuktitut does not mark stress, and intonation is used as a cue for phrasing, while statements and questions are morphologically marked by a suffix added to the verbal root. Conversely, English absolute questions are both prosodically and syntactically marked, whereas the difference between statements and declarative questions is prosodic. To determine the degree of crosslinguistic influence (CLI) and whether CLI is more prevalent in tasks that require access to contextual information, bilinguals and controls performed three perception and two production tasks, with varying degrees of context. Results showed that bilinguals did not differ from controls in their perception of low-pass filtered utterances but diverged in contextualized tasks. In production, bilinguals, as opposed to controls, displayed a reduced use of pitch in the first pitch accent. In a discourse-completion task, they also diverged from controls in the number of non-target-like realizations, particularly in declarative question contexts. These findings demonstrate patterns of prosodic and morphosyntactic CLI and highlight the importance of incorporating contextual information in prosodic studies. Moreover, we show that the absence of tonal variations can be transferred in a stable language contact situation. Finally, the results indicate that comprehension may be hindered for this group of bilinguals when sentence type is not redundantly marked.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".