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Record W2395793944

Building meta-governance for strengthening critical infrastructure in Canada and in the US: The Case of the "Beyond the Border" Initiative

2015· article· en· W2395793944 on OpenAlexaboutno aff
Nicolas Francoeur

Bibliographic record

VenueuO Research (University of Ottawa) · 2015
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceCritical infrastructurePolitical sciencePublic administrationEconomic growthBusinessEconomicsManagementLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.300
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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