A Per-second Investigation of the Interconnectedness between Linguistic and Cognitive Factors Underlying L2 Willingness to Communicate
Bibliographic record
Abstract
Willingness to communicate (WTC) research has recently witnessed a paradigm shift with the more recent studies looking at the shifting and dynamic nature of the variable. A growing body of literature has interpreted such dynamicity from a complex dynamic systems (CDS) perspective. The theory of CDS has four basic properties, one of which, and the focus of this study, is the interconnectedness among subsystems. This property mainly involves the interplay amongst parts of a system, which interact and influence one another, determining the subsequent dynamics in the system. This qualitative, exploratory study employed an idiodynamic method to investigate the interconnectedness of the cognitive and linguistic factors underlying second language (L2) WTC. To this end, 20 participants completed four three-minute monologic speaking tasks while being video-recorded. Immediately after, they viewed their recordings, rated their WTC moment by moment, and explained the WTC changes in stimulated recall interviews. The interviews were coded, and instances where WTC was affected by cognitive and linguistic factors were identified and analysed. Three patterns of interconnectedness emerged: (1) WTC and linguistic factors; (2) WTC and cognitive factors; and (3) WTC, and linguistic and cognitive factors. Findings provide a clearer account of the interconnectedness property in the WTC system, lending support to viewing WTC as a CDS. The article highlights the importance of self-perception and availability of content message, in addition to the above factors, and concludes with a brief discussion of the pedagogical implications for L2 classroom.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".