Bibliographic record
Abstract
This study examines the concept of working knowledge management with respect to the North Vancouver School District as exemplified by their practices related to generating, capturing, and disseminating 'Know How' and promoting informed professionalism. The North Vancouver School District was found to be comparatively "advanced", or knowledge-rich, in terms of its data use and knowledge translation capacity. The thesis explores an important area of school district organization and leadership. It examines the school district's response to issues of accountability and improving student improvement. The case study examines the district's understanding of, and capacity for, working knowledge management. In this setting, one finds educators struggling to acknowledge that their instructional ideas and practice can be made visible and are improvable; struggling to foster a culture of collaboration and interaction within and across schools or among teachers; and struggling to systematically manage their working knowledge. The British Columbia Ministry of Education planning and information processes are dominated with concerns about input and outcome data. The education system appears to ignore schools' instructional practices from enquiry, discourse, and change. I believe that knowledge management literature provides a useful tool to examine school district practices. Working knowledge management practices in this study are used as generic factors to examine education system practices that can facilitate change. The models presented in this thesis together offer vehicles for school district leaders to inform their consideration of how they manage their working knowledge activities.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".