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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it