Knowledge Audit Approach for a Large-Scale Government KM Strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The promise of increased organizational performance has brought about a high level of interest for knowledge management (KM). Organizations and Governments are also actively launching KM projects to meet increasing needs of high quality and responsiveness. This interest has contributed to the development of various aspects of KM, but has also underscored a lack of effective methods, as evidenced by the sheer number of proposed approaches, along with a lingering scepticism about their relevance in practice. In this article, we argue for the necessity for a more global and high level analysis for orienting KM strategic planning and propose different steps to go about it. We used an action research approach in the context of Quebec's efforts in planning a global and integrated KM strategy for managing its water related knowledge. This research project shows that the proposed auditing approach provided a useful guide to identify critical issues and projects in KM planning, particularly in a complex and large-scale governmental environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it