Enhancing Knowledge Management Practices: A Multiple Holistic Case Study with Principals and Teachers in a Western Canadian Province
Bibliographic record
Abstract
The purpose of this multiple holistic case study was to understand how principals and teachers in a school division in a western Canadian province enhance their knowledge management practices. Knowledge management was generally defined as the organization, capture, use, and analysis of the impact of a group’s collective knowledge. The theory guiding this study was Davenport and Prusak’s theory of knowledge management as it explained the relationship between principals and teachers and how they use knowledge management. This qualitative research study was a multiple holistic case study which involved six principals and six teachers in a school division in a western Canadian province. Data were collected using individual interviews, document analysis, and journal prompt reflections. Data analysis included transcription by me, cross-case synthesis, various cycles of codes, categories, sub-categories, and themes. Although the six principals and six teachers were already using knowledge management to move the learning of students forward, the findings indicated what would enhance their knowledge management practices. The findings indicated that the participants used knowledge management practices for goal attainment through connections. Goal attainment was through effectiveness and efficiency while connections were through relationships, involvement, and engagement.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".