The Sustainable Development and Resilience of Socio-Economic System: Conceptualization and Diagnostics of Problems in Conditions of Global Challenges and Shocks
Bibliographic record
Abstract
Today, the issue of ensuring the sustainable development of socio-economic systems is more relevant than ever. The study used an interdisciplinary approach. Based on this approach, it is proposed to interpret the stability and sustainable development of the socio-economic system in the context of global instability as its ability to recover and reorient itself after the impact of external global shocks and challenges due to adaptive internal drivers. The practical implementation of the author's methodological approach to assessing the sustainability and sustainable development of the socio-economic system on the basis of a retrospective section reveals that the most negative impact of the global financial and economic crisis and hostilities was in the context of actual GDP per capita. decline over the past 15 years. The article highlights the main factors for reducing the stability and sustainable development of the socioeconomic system in the context of the shock of the COVID-19 pandemic. The study has its limitations, as it was carried out to a greater extent in the context of the realities and indicators of Ukraine. Similar analyzes are planned for other countries in the future.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".