Improving Real Property Portfolio Planning and Governance at the Department of National Defence
Bibliographic record
Abstract
In 2012, the Office of the Auditor General identified major concerns with the way that the Department of National Defence (DND) manages its real property (RP) portfolio. Subsequently, in 2016, DND centralized the management of all of its RP under the Assistant Deputy Minister (Infrastructure and Environment) and promulgated a long-term comprehensive national RP management strategy. This supervised research project (SRP) conveys a holistic overview of how Canada, the United States, United Kingdom and Australia manage their Defence RP, offers recommendations on how to improve RP management at the DND national level and provides a standard framework for the development of regional plans. The SRP also examines the local and regional effects of Base expansion, redevelopment, and closures on neighbouring communities.[...] The information gathered and the tools provided in this SRP can improve DND’s real property portfolio management strategy, as well as its planning and governance processes to effectively and efficiently address and align national, regional and local requirements, while adequately attending to the effects of Defence establishment expansion, redevelopment and closures on local and regional communities.
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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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".