Preparing teachers for emotional labour: The missing piece in teacher education
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
A quality education for all children and youth is required for the continued advancement of modern civilization. But this outcome is threatened by a growing international teacher shortage. Increased rates of teacher attrition and reduced rates of enrollment in teacher education programs are driving this shortage; however, research suggests that teacher candidates’ lack of preparation for the emotional labour of teaching is another important contributing factor, one which can be addressed in teacher education programs. The aim of this paper is to explore this problem and surface potential solutions. First, the social historical context of teaching is explored as an entry point to inquiry into this topic. Next, through discussion of the emotional nature of teaching, the thesis that teacher candidates must be prepared to handle the emotional labour of teaching during their teacher education program is advanced. Then, a review of the literature surfaces three key content areas which if addressed during teacher preparation can help prepare teacher candidates to handle the emotional labour of teaching: identity development, emotions and teaching, and social-emotional competence. Finally, these components are included in a theory of change for a new program that could be integrated into existing teacher education programs.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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