Building Alberta Infrastructure & Transportation as a Knowledge Intensive Organization
Bibliographic record
Abstract
This paper describes how the Alberta Infrastructure and Transportation’s vision is that the department is a center of excellence that provides modern infrastructure in order to support Alberta’s growth and prosperity. The Department’s senior executive has defined “a center of excellence” within the context of Alberta Infrastructure and Transportation to include creating a working environment that encourages and values knowledge and innovation. In addition, the first priority in the department’s corporate human resource plan is building capacity through supporting learning and development, and ensuring continuity and knowledge transfer. Knowledge management has been described as a set of techniques and practices that facilitate the flow of knowledge into and within an organization. It has also been suggested that there are three components in the knowledge cycle: creating new knowledge, managing existing knowledge, and sharing and transferring knowledge. To become a knowledge intensive organization and a center of excellence, Alberta Infrastructure and Transportation must adopt a coherent, comprehensive strategic approach to all three of these components. This paper presents the department’s strategic knowledge framework that includes: (1) partnering with engineering consultants, contractors, suppliers, academic agencies and regulatory agencies to create and transfer knowledge through initiatives; (2) the management of knowledge; and (3) the transfer of knowledge. The paper concludes with some observations on the knowledge management challenges and opportunities currently faced by the department.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".