Re-conceptualizing the knowledge base for non-native language teachers to cope with negative emotions
Bibliographic record
Abstract
When non-native speakers become second language teachers, emotions play a significant role in the teacher-learning process and throughout their professional lives. However, few teacher education programs explicitly relate emotions to teachers’ knowledge bases to improve their social-emotional skills. Due to the dynamic nature of a knowledge base, teachers can always take an inquiry stance to continuously examine their teaching practices and beliefs. Therefore, this paper takes an inquiry stance to discuss negative emotions in non-native language teachers’ narratives and how they can overcome the potentially negative effects of such emotions by reconceptualizing their personal knowledge base to reinforce the effects of positive emotions and minimize the effects of negative emotions in their teaching practice. As a preliminary study, it aims to raise awareness for teachers to develop social-emotional skills through knowledge-base re-conceptualization and to advocate for reform of language teacher education./ Keywords: emotions, social-emotional skills, language teacher education, knowledge base, non-native teachers
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".