Bibliographic record
Abstract
Purpose Drawn from a recent research study of the Toronto District School Board, this paper aims to examine how the District employs knowledge management to initiate and improve early literacy instruction and achievement. Design/methodology/approach This study draws on Nonaka and Takeuchi's framework to explore how focusing on tacit‐to‐tacit knowledge‐sharing strategies influence early literacy‐based knowledge sharing within and across schools. Data collection involved the collection and analysis of documents used and designed by Early Years Listeracy Project (EYLP) staff members. The second phase engaged a cross‐section of 34 EYLP teachers, administrators and senior TDSB superintendents and EYLP management team members in individual semi‐structured interviews. Participants commented on their experience vis‐à‐vis the various knowledge management strategies used to support its implementation. Data from the interviews was codified, analyzed and summarized and summaries were shared with participants for comment. Findings The District has employed a comprehensive strategy designed to build instructional and leadership capacity via the use of in‐school knowledge activists and informal professional networks. This paper explores the impact of these strategies on school and district‐level teacher and leader learning and organizational culture. Originality/value The overall impact of these strategies for professional and organizational learning and the challenges associated with employing knowledge management within education and the broader public sector are presented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".