Some Crucial and Major Changes in a Post-COVID-19 World as Seen by the AI System Mileva
Bibliographic record
Abstract
Abstract Why use an artificial intelligence (AI) system to determine crucial, major changes in a post-COVID-19 world? Globalisation is both a system and a process characterised by complexity, that is, a referential in which heterogeneous agents are constantly interacting. It therefore requires an integral and dynamic approach, and even more so a tool in tune with complexity. That is the case of the AI system Mileva, specifically designed for tackling complexity, highlighting the fabric of its reality, its core issues, and to forecast the probabilities of the different possible evolutions. In this chapter, the authors first briefly describe globalisation with regard to complexity, at the crossroads of computational complexity theory and sociological complexity theory (Edgar Morin). The authors then present the AI system Mileva, its key principles and the main lines of its architecture. Finally, the aforementioned points will be illustrated by two examples of analyses provided by Mileva on the issue of major changes in a post-COVID-19 world: the situation of the international organisations and that of the world of work in relation to health, environment, development, and democracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".