Pedagogical Narrations and Leadership in Early Childhood Education as Thinking in Moments of Not Knowing
Bibliographic record
Abstract
In this article, I draw from a recent research project that examined how a reconceptualized notion of leadership in early childhood education (ECE) is enacted through the practice of pedagogical narration.Pedagogical narration involves a process through which early childhood educators create and share narratives about significant pedagogical occurrences with children from their early childhood settings with the purpose of engaging others in critical dialogue where construction of children's identities and the values embedded in ECE practices are made visible and open for disputation and renewal.Here I present a particular dimension of leadership enactment that is associated with the practice of pedagogical narration, and I relate this leadership to thinking in moments of not knowing.By providing an example of how leadership was enacted, I wish to illuminate the potentiality of the practice of pedagogical narration to orient educators toward complexity and thinking that is triggered by encounters with unexpected pedagogical situations.Such encounters have the potential to initiate educational judgment that is inspired by events and not by following known rules and standards.They invite thinking beyond generalizations and clichés and they orient future (more) responsive and ethically rich pedagogical ventures in ECE contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".