Bibliographic record
Abstract
This paper explores the concept and significance of emotional reflexivity in language education, emphasizing how teachers' emotions shape their pedagogical decisions, classroom interactions, and professional identities. Emotional reflexivity, understood as the critical examination of one's own emotions within teaching contexts, integrates personal reflection with social critique, highlighting how institutional and cultural norms influence teachers' emotional responses. The analysis identifies common emotional challenges faced by second language teachers, including linguistic insecurity among non-native speakers, cultural tensions in multicultural classrooms, and emotional exhaustion stemming from workload pressures. By examining the interplay between teachers' emotions and classroom practices, the paper reveals how emotional states influence decision-making processes, teacher-student relationships, classroom atmosphere, and the willingness to address social issues. To address these emotional dimensions effectively, several strategies for fostering emotional reflexivity are proposed, such as reflective journaling, collaborative dialogue within professional communities, scenario re-enactments, and critical questioning of one's emotional reactions. Ultimately, the paper argues for the integration of emotional reflexivity into teacher education programs as a means of promoting emotionally responsive, ethical, and equity-oriented language teaching. Through cultivating emotional reflexivity, teachers are better equipped to navigate the complexities of their roles, creating inclusive and empathetic educational environments.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".