Bibliographic record
Abstract
Current research on second language (L2) anxiety solely deals with the vague fears. Those research results do notreflect L2 learners’ real concerns or furthermore help them to reduce the “tension” rather than anxiety. The researcherconsiders the need to distinguish L2 writing tension from L2 writing anxiety. Furthermore, this study attempts toinfuse the pragmatic aspect by adding two categories of questions related to actual situations and classroom activitiesto the Foreign Language Writing Anxiety Questionnaire (Tsai, 2012). The results of the Bivarited correlation testsshow both the inter-category and intra-category reach the significant level at .05 or better. Thus, the New ForeignLanguage Writing Anxiety Questionnaire (NFLWAQ, Appendix 1) is formed. Notably, the L2 writing tension in thisstudy is significantly higher than the foreign language writing anxiety in the overall group as well as every individualgroup at the significant level of .05 or better. The results indicate that the participants worry about real situations andclassroom activities more than the vague fears from nowhere. The peer review activity is recognized as the leastpressure source that L2 writing teachers might want to practice it from time to time to reduce students’ tension.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".