Preserving and Using Institutional Memory Through Knowledge Management Practices
Bibliographic record
Abstract
This synthesis report documents practices regarding the preservation and use of institutional memory through the knowledge management (KM) practices of U.S. and Canadian transportation agencies. It identifies the practices for the effective organization, management, and transmission of materials, knowledge, and resources that are in the unique possession of individual offices and employees. Issues covered include: Does the agency have a KM program? Who has overall responsibility of KM practices? Is there an agency library, and sufficient staff or financial resources? Have materials to be retained been identified? Are there written guidelines for the retention of historical materials? What tools are available for capturing and storing KM resources? Exemplary practices for KM from other professions are included. Surveys were returned from 38 transportation agencies. A literature survey was conducted to identify trends and practices within the transportation community. In addition, three case studies are provided as examples of state departments of transportation with KM programs at various stages. Detailed survey responses can be found in the appendices, as well as those sections covering the literature survey.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".