Perceived L2 oral fluency, working memory, foreign language enjoyment and foreign language anxiety: identifying distinct patterns of relationship
Bibliographic record
Abstract
Abstract L2 oral fluency is a core aspect of general oral proficiency. However, it represents a distinct challenge for L2 speakers as it requires a real-time efficient allocation of cognitive resources. Specifically, working memory (WM) plays a crucial role in speech production. These cognitive resources are limited and vary from one speaker to another. Additionally, they have long been shown to interact with foreign language anxiety and foreign language enjoyment, which interact with various aspects of L2 oral production. To our knowledge, no previous studies have investigated the relationship that might exist between perceived L2 oral fluency ; i.e. listeners’ judgments about L2 speakers’ fluency, WM, foreign language anxiety and foreign language enjoyment. To fill this gap, 78 ESL French-speaking adults were subjected to a picture-based narration task. A flowchart scheme was used to measure perceived L2 oral fluency. WM was measured using a numerical span test, and foreign language anxiety and foreign language enjoyment were measured through a questionnaire. Results show a distinct pattern of interaction between the variables. More specifically, foreign language anxiety significantly predicted perceived L2 oral fluency among participants with a weaker WM, whereas foreign language enjoyment significantly predicted L2 oral fluency among participants with a stronger WM capacity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".