THE NATURE OF THE RISK: PERCEPTIONS ACROSS G7 COUNTRIES REGARDING RISKS FROM INFORMATION TECHNOLOGY VERSUS THOSEARISING FROM CLIMATE CHANGE, 2022-2025
Bibliographic record
Abstract
Today, the world is facing a world of risks. No matter the country, the riskiest elements are perceived to emanate from the realms of information technology and climate change. In this article, we look at how the nature of today’s risks are perceived across the G7 Nations - Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States – and how this can – and is – impacting strategic management decision-making. We begin with a look at how we individually and collectively process risk, and specifically, risks that are spurred both by information technology, specifically from cyberattacks and artificial intelligence, and by climate change in general and specifically, as it relates to extreme weather and forest fires and the destruction of natural habitats. Then, using a database constructed from the four years of existence of the Munich Security Index, we examine how the perception of both IT-related and climate-driven risks has elevated between 2022-2025 in the G7 countries, but with important intercountry differences and discrepancies between IT-related risks and those coming from “Mother Nature.” The results of this analysis and then discussed, along with directions for future research in this area and the implications of all of this for strategic management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".