Knowledge Management Strategy and Framework (Stategie et cadre de gestion du savoir)
Bibliographic record
Abstract
Knowledge management (KM) is the strategic management of the creation and use of knowledge for increased innovation, value and excellence. The KM Strategy supports the Defence Research and Development (R&D) Canada (DRDC) vision of becoming the best in defence R&D through five objectives: Clarifying and focusing the mission so that employees and management are able to understand the intention and prioritize their efforts; Developing corporate information management tools and information seeking expertise as building blocks for KM; Accessing and sharing internal expertise; Enhancing the exchange and access to foreign defence R&D information and knowledge; and Establishing a mutual vision for defence R&D with Canadian Forces clients. The DRDC KM Framework has four components: (1) establishing and nurturing a workplace environment that is conducive to knowledge sharing and creation, i.e., the 'knowledge environment'; (2) tools and systems to access and share information and knowledge; (3) establishing and nurturing relationships for the creation and exchange of knowledge; and (4) the ability to develop the skills and expertise of employees through learning strategies.
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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".