Preparing teachers for emotional labour: The missing piece in teacher education
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.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".