Mentoring Narratives ON-LINE:Teaching the Principalship
Bibliographic record
Abstract
The need to develop new models for preparation of school administrators has been a prominent concern in educational discourse in the last decade. Having been criticized for the inadequate preparation of the school leadership cadre, academic departments responsible for training future school administrators have had to revisit their approaches and to reframe their teaching philosophies to ensure the readiness of their graduates for the challenges and complexities of school leadership. This article reports on the new model of principals' training that has been used in York University's Principals' Qualification Program (PQP) from the late 1990s onward. One component of the program brings traditional case methodology into a computer-mediated/on-line environment. The on-line cases are narratives from the everyday lives of the Ontario school administrators who serve as mentors in the on-line environment. Situating our discussion within the context of the rapidly changing educational landscape of Ontario, we focus on the PQP model to explore experientially generated case narratives as one method for teaching and learning the work of the local school administrator. We focus particularly on the teaching and learning embedded in computer-mediated or on-line case narratives used in training teachers for school leadership. We argue that the complexities of school leadershipthe social, cultural, relational, ethical and moral context of school leadershipcan be taught effectively through the reflective processes of on-line case narratives. We seek to contribute to the ongoing dialogue on the potential of new pedagogies and new technologies to help prepare the competent and responsible leaders for tomorrow's schools.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".