Bibliographic record
Abstract
After nearly a decade of military commitment to war, counterinsurgency, and stability operations in Afghanistan and elsewhere, the Canadian government in 2010 initiated a shift in its national security and economic policies that would ultimately lead to a significant contraction in the total level of funding invested in national defence. One of the first steps taken was the issuance of a directive to the Department of National Defence (DND) to undertake an internal audit of its recent organizational behaviour. Specifically, the Canadian Forces Transformation Team (CFTT), then under the direction of Lieutenant General Andrew Leslie, was ordered to identify areas where DND “could reduce overhead and improve efficiency and effectiveness, [in order] to allow reinvestment from within for future operational capability despite constrained resources”. 1 For General Leslie and his team, however, finding a perfect solution to this problem was impossible. Already battered by the realities of global recession, changing policies, government deficit fighting measures, and an impending departmental strategic review, there was no doubt that difficult choices would have to be made. Therefore, the recommendations put forth in General Leslie’s Report on Transformation surely appeared draconian, and not unexpectedly invited immediate criticism from both the affected stakeholders as well as the
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.788 | 0.652 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".