Teacher Creativity: When Professional Coherence Supports Beautiful Risks
Bibliographic record
Abstract
Environmental, cultural, and social issues are becoming increasingly complex, and the educational context is no exception to this trend. The relevance of teachers' creativity in examining situations from different angles, in imagining new approaches, in adapting to the varied needs of students, and in training them so that they too can grasp the teeming complexity seems obvious. However, creativity sometimes seems to be taken for granted among teachers and educational programs leave a gap around this theme. Since the scientific literature tends to show that teachers' creativity is still little explored in educational contexts, this doctoral research studies its manifestations within a group of teachers enrolled in a professional master's program in preschool and elementary education (Université de Sherbrooke, Canada). Within the framework of this program, each one elaborates a professional development project over a three-year period. Using a variety of authentic data sources (observations in natural occurring situations, reflective writing by participants, and semi-structured interviews), their creative process is documented and analyzed. This multiple-case study (n = 9) that draws on the concept of creativity as related to that of professional coherence reveals that the pursuit of greater professional coherence not only enables the implementation of creative skills to foster teachers' professional development, but also elicits beautiful risk-taking.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.011 |
| 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".