A Contribution to Knowledge Management through a Canadian Research Initiative
Bibliographic record
Abstract
This paper describes how knowledge is considered to be an asset and that it has value and should be considered an integral part of asset management. In fact, all three levels of asset management, strategic, network/system wide and project/location specific, have knowledge explicitly or implicitly embedded in each activity. Knowledge is generally viewed as being in one of three basic types: (1) explicit knowledge, which is essentially documented information; (2) implicit knowledge, or “know how”; and (3) tacit knowledge, which is contained in the human subconscious (e.g., creative or innovative capabilities). Knowledge management has a variety of definitions. Essentially, it is a process for the effective utilization of available knowledge to produce results, both short term and/or long term. Organizational approaches to knowledge management generally consider explicit knowledge as having a lower value than implicit knowledge. The most valuable, arguably, is tacit knowledge, but it is also the most difficult to maintain and vulnerable to loss. One of the major pitfalls is the belief that a knowledge management system will build organizational culture. The reverse holds, where organizational culture must exist to achieve success. A Canadian research initiative which views knowledge management as a vital part of its success is the Centre for Pavement and Transportation Technology (CPATT). This Centre was made possible by an unprecedented $9 million funding package from Federal, Provincial, Municipal and private sector partners. It has articulated a vision focused on emerging and innovative technologies, a state-of-the-art research infrastructure, training and education, and sustainability in research capabilities, programs and partnerships. CPATT’s approach to knowledge management incorporates the following strategic elements: (1) identifying the activities of CPATT within explicit, implicit and tacit types of knowledge; (2) defining knowledge management (KM) and succession planning as synonymous; (3) establishing the key reasons for KM; (4) identifying the key components for proper KM; (5) establishing the cost-effectiveness of KM; (6) ongoing program of training and skills development for students and staff; and (7) developing measurable, key performance indicators for KM systems.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".