A case study of district leadership using knowledge management for educational change
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the work of district leadership of a large Canadian school district in becoming a learning organization over four years using knowledge management practices. Design/methodology/approach A qualitative study conducted from 2000‐2004 using a naturalistic research paradigm with the underlying principles of grounded theory. Data were collected from a sample of six supervisory officers through individual and focus group interviews. Findings Using knowledge management practices, the senior leaders of a large school district organically developed a unified new amalgamated super‐district. They redefined their roles from managers to knowledge leaders in order to reshape the district into a learning organization that could positively respond to the continual changes being rained down on them. Practical implications This paper offers insights that are both theoretical and practical on how senior leaders transform their role from operational managers to knowledge leaders for school improvement. The conceptual framework proves valuable in understanding how change can work in practice. Research limitations/implications Although the study is limited by the specific context from which data were drawn, it offers useful lessons and direction for large districts undergoing major reforms. Originality/value This paper highlights the role of senior leadership as knowledge leaders managing a district towards becoming a learning organization via organic processes that promote knowledge flow.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".