Creativity and pedagogical innovation: Exploring teachers’ experiences of risk-taking
Bibliographic record
Abstract
Objective: The purpose of this paper is to share the results of research into the experience of teacher risk-taking in the classroom. The development of children as risk-takers is featured prominently in curriculum documents and reports calling for the competencies of 21st century learning. Teachers are expected to become 21st century learners who model risk-taking. The repeated calls for the development of risk-taking students through the modelling of risk-taking teachers makes the experience of risk an important pedagogical question. However, 21st century learning documents do not take up substantively the meaning of teacher risk-taking.Research Design: Phenomenological research is concerned with the unique and the individual and in that regards each teacher-participant represents particular perceptions of risk-taking experiences and responses to risk in the classroom. The six (6) teacher-participants responded to a call distributed widely to teaching staff in a Canadian school district. The inquiry relied on phenomenological interviews and experiential life world material. In this paper three phenomenological themes are described: risk and readiness; risk and the in-between spaces of pedagogy, and risk as exploration and finding a way. This research allows us to understand teachers’ lived experience rather than assume the meaning of the terms risk and 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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".