THE RELATIONSHIP BETWEEN L1 FLUENCY AND L2 FLUENCY DEVELOPMENT
Bibliographic record
Abstract
A fundamental question in the study of second language (L2) fluency is the extent to which temporal characteristics of speakers’ first language (L1) productions predict the same characteristics in the L2. A close relationship between a speaker’s L1 and L2 temporal characteristics would suggest that fluency is governed by an underlying trait. This longitudinal investigation compared L1 and L2 English fluency at three times over 2 years in Russian- and Ukrainian- (which we will refer to here as Slavic) and Mandarin-speaking adult immigrants to Canada. Fluency ratings of narratives by trained judges indicated a relationship between the L1 and the L2 in the initial stages of L2 exposure, although this relationship was found to be stronger in the Slavic than in the Mandarin learners. Pauses per second, speech rate, and pruned syllables per second were all related to the listeners’ judgments in both languages, although vowel durations were not. Between-group differences may reflect differential exposure to spoken English and a closer relationship between Slavic languages and English than between Mandarin and English. Suggestions for pedagogical interventions and further research are also proposed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".