Strategic Analysis of the Appointment Requirements for the Minister of National Defence: Should he wear the uniform as well?
Bibliographic record
Abstract
November 4, 2015 marked the day that Canadian Armed Forces veteran, Harjit Singh Sajjan, was sworn in as Minister of National Defence.[1] The Canadian population applauded the appointment made by Justin Trudeau because it appeared natural to have a Minister of National Defence who had previously worn the Canadian Armed Forces uniform. The support was an unexplained phenomenon; for some reason, the Canadian public was excited, curious and confident in the ministry’s new leader because of his close ties to the department. Though many veterans have held the position of Minister of National Defence, it has never been a requirement to be a veteran to hold the position. Many scholars believe that a Minister of National Defence who has prior military experience is more of an asset in this role. Despite this belief, there remains no military prerequisites for the Minister of National Defence position. This paper will analyze the role of the Minister of National Defence and discuss why having a member of parliament who is also a veteran is not a practical policy to continue implementing in future governments. [1] Statement by the Prime Minister of Canada Following the Swearing-in of the 29th Ministry. (n.d.). Retrieved November 18, 2017, from http://www.marketwired.com/press-release/statement-prime-minister-canada-following-swearing-29th-ministry-2070297.htm
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".