Risk management in public sector research: approach and lessons learned at a national research organization
Bibliographic record
Abstract
As the Canadian federal government's main research body and a public sector agency, the National Research Council (NRC) must manage numerous strategic as well as operational risks, including those at the project, program and portfolio levels. Such risks might arise from political and other stakeholder interests, intellectual property ownership and policy, funding structures, public perceptions of science and technology, occupational health and safety, management of highly qualified personnel, availability of receptor capacity for research being undertaken, and unknown markets for very new research areas, to name a few. Varying risk management practices have existed across NRC institutes and programs in the past as a result of the relative autonomy afforded to these groups. In seeking a more systematic approach, driven by both external and internal interests, NRC researched best practices, models and frameworks for risk management. NRC needed an appropriate model and approach for managing risk that could be applied throughout different levels and within the various arenas of its activities. The approach selected is based on the concept of enterprise risk management, allowing NRC to look not only at specific areas of risk but the larger picture – effectively assessing, controlling, exploiting and monitoring risks from all sources that might threaten the achievement of its goals. At the same time, such an approach also ensures that potential opportunities that could facilitate achievement of its goals are not missed. This paper shares some of NRC's findings of its research (including best practices), describes its current framework and approach, as well as some of its challenges.
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 | 0.203 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.013 | 0.052 |
| Scholarly communication | 0.048 | 0.025 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".