Knowledge Governance: Addressing Complexity Throughout the Knowledge Processing Cycle
Bibliographic record
Abstract
Effective knowledge governance requires organizations to develop core competencies as well as organizational structures and policies. Knowledge governance mechanisms must be put into place for the creation, sharing, storage, and transfer of knowledge, which in turn foster synergy and create value. However, knowledge governance needs to go beyond the “simple” knowledge management (KM) process cycle to also encompass organizational learning and improvement processes for both explicit and tacit knowledge. An integrated knowledge governance (IKG) model is needed for the processes of knowledge creation, sharing/dissemination, organizing/storing, using/reusing, and learning for organizational improvement. An IKG can also better address the complexity in governing knowledge at three levels (individual, group, and organizational) using both formal and informal approaches. This chapter uses the Evans et al. holistic knowledge process cycle to map specific governance activities to each knowledge process as it provides a holistic view of the knowledge life cycle by building on previous life cycles models and by extending previous models to integrate the notion of second order or double loop learning. Best practices from research and an illustrative organizational implementation will be used to highlight recommendations. The chapter will conclude with some research gaps that still remain to be addressed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".