The New Global Economy between a Well-Planned Journey and a Chaotic One. Under the Impact of both Climate Change and the Post Pandemic, the NGE: A More Complex and Less Predictable System
Bibliographic record
Abstract
The article is devoted to the understanding of the emergence of new traits of the global economy under the impact of climate change and the COVID pandemic. Economic research is nowadays primarily oriented towards the unpredictable and sometimes confusing situations related to the consequences of climate change. Global economy is a complex behavior with a new dynamic. If green energy is to be the main predictable feature we are confronted with three questions: is it robust, sustainable and resilient? The new global economy is not about a bright future; it is about selecting a positive norm that indicates today a positive behavior of it. Hydrogen fossil issue, electricity becoming a tradable commodity, the new role of nuclear energy as a crucial complement to renewables are among the main contributors in redesigning energy markets. We can safely say that by mid-century the world will need to remake its energy system. Indeed, while the science of climate change is today firmly established on powerful truths, the final outcome is not a simple extension of present-day trends. The environment, under the impact of climate change, is presently in a disordered phase of transition. Global disorder should not be inevitable even if critical thresholds seem to be inevitable. The obvious solution is cooperation out of what we believe to be true. We have to act in the presence of uncertainty and often that means that a better situation could be simply an unattainable one.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".