Leveraging Knowledge Management Tools to Support Security Risk Management in the Department of Homeland Security
Bibliographic record
Abstract
Abstract : This thesis examines Knowledge Management (KM) initiatives at the Canadian Institutes of Health Research (CIHR), the United Kingdom (UK), and the National Aeronautics and Space Administration (NASA). The first goal was to identify existing KM approaches that would foster higher levels of knowledge sharing and collaboration among security risk management practitioners within Department of Homeland Security (DHS) agencies to enhance risk informed decision-making activities. Through the analysis of the three case studies, it was discovered that organizational culture, more than any particular KM process or enabling technology is responsible for moderating the level of knowledge sharing. The KM strategies, policies and implementation mechanisms explored in the three case studies are good models for DHS to consider in order to reduce agencies' uncertainty, aiding decision making and bolstering effectiveness. The Risk Knowledge Management System (RKMS) called for in the DHS Integrated Risk Management Directive will require similar implementation and support structures for DHS to overcome the cultural, process, security, and funding obstacles experienced by the United Kingdom, Canada, and NASA. By using these case studies as models and reflecting on their experiences, DHS will be better positioned to effectively implement and adopt proven KM policies on an agency-wide basis.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".