Bibliographic record
Abstract
The Department of National Defence made some progress in procurement in 2016 despite obstacles that included a continued drop in spending, the advent of a new federal Liberal government and uncertainty over the outcome of the Defence Policy Review. Four trends affected defence acquisitions in 2016. These include an ongoing slippage in recapitalizing the Canadian Armed Forces, some encouraging moves made on the shipbuilding and fighter jet files, mixed progress on implementing the 2014 Defence Procurement Strategy, and uncertainty over the Defence Policy Review. It is also too early to tell how the Trudeau government’s Policy on Results, known as the “deliverology” approach, will play out for defence procurement. However, Budget 2016’s major focus was not on defence, and it shifted some funding for capital equipment to a new endpoint of 2045. This suggests that delay in the overall defence procurement program continues. While the Liberals kept their pledge to make investment in the Royal Canadian Navy a priority, they also made good last year on a negative promise – not to purchase the F-35 stealth fighter bomber. However, further slowing things is the Liberals’ refusal to launch a competition to replace it until the Defence Policy Review is published. The government has made this situation more fraught with its intention to buy 18 Boeing Super Hornet fighter jets as interim aircraft, since Liberal policy requires the Royal Canadian Air Force to be capable of meeting both NORAD’s and NATO’s operational needs simultaneously. Prior to the release of the new defence policy, both the interim and permanent fighter aircraft projects lacked adequate funding. They were among several large projects that have been approved, but have not yet moved to the contract stage, and whose budgets were inadequate to move forward. Adding to this mix is the fact that a government-wide effort initiated in 2014 to streamline the defence procurement process made no progress in 2016, and a significant number of other prospective projects were not included in the DND investment plan. The subsequent Defence Policy Review has addressed the funding issues, but they were problematic throughout 2016. Not all is gloom and doom, however. A contract for 16 fixed-wing search and rescue aircraft was awarded, modernization of all of the Halifax-class frigates was completed last year, the number of light armoured vehicles deployed in the field rose from 64 to 262, 10 maritime helicopters were added to the fleet in December, and the new medium-to-heavy lift helicopters carried out their first mission by responding to the Fort McMurray wildfires.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.130 | 0.087 |
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".