Bibliographic record
Abstract
Although the knowledge society is evolving rapidly, uptake of knowledge management principles and practice in the public sector have lagged well behind that in the private sector. To help overcome this difficulty, the knowledge management mantra that industrial-era cultures must change in order for KM to succeed is reformulated into a new paradigm: Within an existing culture, how can knowledge management increase the value of organizational knowledge and the productivity of knowledge work? The paradigm uses the Cynefin sense-making framework as a foundation for knowledge manageability. Four knowledge manageability regimes are described: authoritative hierarchy (use of explicit knowledge is authorized through organizational decisions), organizational structure (explicit knowledge is codified and interpreted in the context of organizational processes), negotiated agreements (tacit knowledge is exchanged among individuals and within communities to validate new knowledge), and responsible autonomy (innate knowledge is voluntarily used by individuals to create new knowledge). Most organizations use all four regimes, each of which requires a different approach to management. The chapter also describes methods for transferring knowledge across the regions, from creation to application. The knowledge manageability framework encompasses a spectrum from dynamic, unstructured organizational environments to relatively inflexible, highly structured environments. It provides a robust, multi-dimensional framework for managing knowledge and knowledge work across diverse organizational contexts. By avoiding the need to change inherently structured culture and work processes, it greatly reduces the challenges associated with implementing knowledge management in public-sector organizations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".