Antifragility of the national economy: A heuristic assessment
Bibliographic record
Abstract
The geopolitical turbulence and the implementation of large-scale international sanctions dictate the need to assess the degree of readiness of the states to a longterm civilisational confrontation. The article aims to construct and test a new analytical tool – antifragility index of the national economy. Methodologically, the research is based on the idea that in the presence of several industries, the national economy obtains a functional foundation and a possibility to exist autonomously in conditions of disrupted international trade relations. To put this idea into practice, the article proposes a heuristic algorithm for constructing an antifragility index of the economy taking into account the priority of such industries as agriculture, pharmaceuticals industry, production of means of labour, and mineral extraction. Based on the national statistics of eight states – the USA, Canada, Great Britain, Germany, France, Switzerland, Brazil and Russia – the paper presents pilot calculations of the index. According to the results, only Russia’s index showed an upward trend in 2003–2020, while in the other seven countries it went down. The antifragility index is shown to have an ability to capture the peculiarities of political cycles and event shocks in the world economy. The research provides empirical evidence that the change of the leading country, amongst other things, is associated with the accumulation of structural disproportions in the economy: the weakening of its foundation made up of vital industries and excessive complication of the industrial superstructure in the form of the non-productive sphere. The paper proposes scaling up the constructed index to a broader sample of countries in order to clarify the regional disposition of forces in the global geopolitical space.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".