Willingness to Communicate and Second Language Speech Fluency: A Complex Dynamic Systems Perspective
Bibliographic record
Abstract
The application of complex dynamic systems theory (CDST) in second language (L2) research has recently gained ground, instigating a growing series of studies investigating the complex and dynamic nature of individual difference (ID) variables, such as WTC (willingness to communicate). Fewer dynamically informed investigations, however, have targeted L2 performance constructs, like speech fluency. Both WTC and L2 fluency presumably influence communications in a L2 and have been argued to retain cognitive and affective bases Despite these, little has been done to address such dynamics, particularly from a complex dynamic systems (CDS) perspective. To bridge this gap, the present exploratory study employed an idiodynamic methodology, informed by CDST, to monitor WTC and fluency changes during three-minute, mainly monologic speaking tasks, with an emphasis on the dynamics of change in interaction with temporal measures of speech, including mean length of runs (MLRs), speech rate (SR), and pause phenomena. An investigation of 882 cases of interplay between WTC changes and fluent/dysfluent speech samples revealed an existing interaction, which took on four different forms. Results also indicated that the interaction is of a dynamic one, and is mostly two-way, direct and indirect, unpredictable, and interdependently multilayered. Further, both variables as well as their interplay shared and exhibited such properties as dynamicity, nonlinearity, interconnectedness, and formation of attractor states, all of which are characteristic of complex dynamic systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".