Building meta-governance for strengthening critical infrastructure in Canada and in the US: The Case of the "Beyond the Border" Initiative
Bibliographic record
Abstract
This paper provides a study on the nature of trust as it relates to the theory and practice of meta-governance in the field of critical infrastructure protection.By using the Beyond the Border initiative as a case study, it will be argued that the primary goal of meta-governance in such an environment is to build and sustain trust in governance networks, which is achieved through an equilibrium between process design and institutional management regulatory approaches.Applying examples from the many programs and activities organized under the umbrella of Beyond the Border, such as the Canada-US Resiliency Experiments (CAUSE), the Canadian Critical Infrastructure Information Gateway, and others, the relationship between transaction costs and trust will be assessed.Furthermore, the significance of corporate social responsibility will be argued as an important element of trust-building in a meta-governance setting focused on critical infrastructure.The evidence shown through the Beyond the Border initiative demonstrates that the theory and practice of meta-governance are not so far apart and that many lessons on the links between trust, transaction costs, corporate social responsibility and meta-governance can be learned from this relatively new international development in the field of critical infrastructure protection studies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".