Reproducing successful rituals in bad times: Exploring emotional interactions of a new science teacher
Bibliographic record
Abstract
Abstract Teaching is emotional work. This is especially the case in the first years of teaching when new teachers are particularly vulnerable. By understanding changes in teacher emotions in the early years of teaching, we hope to identify strategies that might ultimately reduce teacher attrition. As part of a larger study of the transition of new teachers to the profession, this ethnographic case study explores how a new science teacher produced and reproduced positive emotional interaction rituals with her students in her first year of teaching. We show how dialogical interactions were positive and satisfying experiences for the teacher, and how they were reproduced successfully in different contexts. We also illustrate how both teacher and students used humor to create a structure for dialogical interactions. During these successful interactions, the students used shared resources to satisfy their teacher that they were engaging in the relevant science content. The implications of what we have learned for the professional development of new teachers are discussed in relation to an expanded understanding of teacher emotions. © 2011 Wiley Periodicals, Inc. Sci Ed 95:745–765, 2011
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".