Second language fluency and its underlying cognitive and social determinants
Bibliographic record
Abstract
Abstract In studying second language (L2) fluency attainment, researchers typically address questions about temporal and hesitation phenomena in a descriptive manner, cataloguing which features appear under which learning circumstances. The goal of this paper is to present a perspective on L2 fluency that goes beyond description by exploring a potential explanatory framework for understanding L2 fluency. This framework focuses on the cognitive processing that underlies the manifestation of fluency and disfluency, and on the ways social context might contribute to shaping fluency attainment. The framework provides a dynamical systems perspective of fluency and its development, with specific consequences for a research program on L2 fluency. This framework gives rise to new questions because of its focus on the intimate link between cognitive fluency and utterance fluency, that is, between measures of the speed, efficiency and fluidity of the cognitive processes thought to underlie implementation of the speech act and measures of the oral fluency of that speech act. Moreover, it is argued that cognitive and utterance fluency need to be situated in the social context of communication in order to take into account the role played by the pragmatic and the sociolinguistic nature of communication in shaping L2 fluency development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".