Beyond the Border Action Plan: A Tool for Enhanced Canada-U.S. Cooperation on Critical Infrastructure and Cyber Security - Or More Window Dressing, The
Bibliographic record
Abstract
While there is ample recognition in both countries of the deep integration of the Canadian and American economies, our mutual reliance on an intricate web of interconnected physical and cyber infrastructure is often overlooked.With few exceptions, governments and the private sector have a limited understanding of the complex interdependencies between these shared systems and networks, and the enormous economic fallout that can result from major infrastructure failures.Canada and the United States have made only modest progress to date in addressing issues related to their shared critical infrastructures and cyber systems.Neither country has devoted sufficient resources nor shown a sustained commitment to making shared infrastructures more resilient and less vulnerable to failure or attack.Both governments have been slow to put in place effective policies and procedures to anticipate, manage, and recover from major crossborder infrastructure disruptions.Joint initiatives in the Beyond the Border Action Plan ("BTBAP")' and the Canada-United States Action Plan for Critical Infrastructure ("CUSCI"), 2 have not been given priority by senior officials in either country and have failed to set out a coherent set of deliverables, deadlines, and accountabilities.This, in turn, has left private sector stakeholders to fend largely for themselves, with little direction from government.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 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".