One Horse Race? A study of interoperability in Canada's Future Fighter Capability Project
Bibliographic record
Abstract
Through the $19 billion Future Fighter Capability Project, the Government of Canada will procure a replacement for the CF-18.However, the requirement that the future fighter be "seamlessly interoperable" with key allies calls into question whether the competitive selection process can be run in good faith. 1 This study argues that contemporary Canadian defence policy is oriented around partnerships with other states, especially the US, and that interoperability would therefore best be attained through the selection of a fifth-generation American platform.However, it is unclear that the FFCP evaluation criteria, which include mandatory and rated technical requirements as well as pillars for cost and industrial offsets, account for high-end tactical networking and new allied technical standards.The FFCP may result in the acquisition of a type that prevents the CAF from interoperating "seamlessly" with allies over its lifecycle.This risk undermines Canada's reliance on partnerships for national defence.1. Introduction………………………………………………………………………………......…5 2. Literature review……..………………………………………………………………………..10 Replacing the CF-18……………………………………………………………………..10 The F-35 as a topic of inquiry.…………………………………………………………...20 Neoclassical realism…………………………………………………………….……….22 Interoperability in the Canadian context…………………………………………………25Towards a definition of interoperability…………………………………………………31 LISI interoperability model……………………………………………………………...
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".