QUALITY OF LIFE AS A FACTOR FOR INTEGRATION OF RESILIENCE RESEARCH OF ENERGY, SOCIO-ECOLOGICAL AND SOCIO-ECONOMIC SYSTEMS
Bibliographic record
Abstract
Рассматривается понятие устойчивости в смысле «Resilience» и связанные с ним понятия энергетической и экологической безопасности. Предлагается рассматривать качество жизни как фактор интеграции исследований устойчивости энергетических, социо-экологических и социо-экономических систем. Вводятся критерии устойчивости энергетических, экологических и социальных систем. Когнитивное моделирование рассматривается как один из основных инструментов исследований устойчивости. Приводятся примеры когнитивного моделирования. The concept of resilience and related concepts of energy and environmental safety are considered. It is proposed to use the quality of life as a integration factor of resilience research of energy, socio-ecological and socio-economic systems. Criteria for the resilience of energy, ecological and social systems are introduced. Cognitive modeling is seen as one of the main tools in resilience research. Examples of cognitive modeling are given.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".