Reply to Shao, Stockinger, Marsh and Pekrun (2023). Applying control-value theory for examining multiple emotions in L2 classrooms: Validating the Achievement Emotions Questionnaire – Second Language Learning
Bibliographic record
Abstract
Shao et al. (2023) make a number of critical comments on our previous research on foreign language (FL) emotions, but also add debatable claims, present an inaccurate view of existing research and present an instrument, the Achievement Emotion Questionnaire – Second Language Learning (AEQ-L2L), that does not capture the full range of habitual positive and negative emotions in regular FL classrooms by focusing exclusively on learner emotions during exams. We agree with the authors that some early scales had unclear factor structures but claiming that therefore these scales are invalid and unreliable is unjustified. We do not deny that the AEQ can provide a comprehensive measure of emotion, but it does not prioritize the context which is fundamental in research on FL learners’ classroom emotions. Moreover, the AEQ-L2L is too long to be reasonably included in complex studies.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.028 | 0.050 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".