Language teacher perspectives on stress and coping
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
Abstract Every person's response to adversity is unique. Whereas some come out stronger as a result of responding to a challenge, others find their fundamental assumptions about themselves and their lives severely challenged. In education, while some teachers might burn out and leave the profession precipitously, many survive the challenges and transform teaching into their lifelong passion. What factors help explain why some language teachers remain resilient and experience growth after trauma, while others suffer a sense of loss, depleted psychological resources, and ultimately succumb to the pressures of the job, leaving the profession or burning out? The purpose of this study was to seek answers to this question in the context of teaching during the Covid‐19 pandemic, which represents a specific unprecedented type of adversity. To do this, 765 foreign language teachers worldwide answered an online questionnaire that asked three open‐ended questions about the stressors and uplifts they were experiencing during the first few months of the pandemic. Respondents were invited to offer their advice to other language teachers who were facing the challenge of teaching during this time.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 it