Global Social Sustainability and Inclusion: The “Voice” of Social and Environmental Imbalances
Bibliographic record
Abstract
Background: Global environmental and social research strengthens the protection of people and the environment, develops national capacity for social and environmental management and enables significant progress in terms of transparency, accountability, nondiscrimination and public participation. The support of the general public plays a key role, as it contributes to making public institutions more transparent, accountable and efficient and promotes ground-breaking solutions to complex development challenges. Citizen engagement seems particularly vital throughout the crises such as the COVID-19 pandemic, as the efficiency of response efforts may frequently depend upon micro-level behavioral changes. The objective of this paper is to provide a complex evaluation and rating of countries based on the social component of the global inclusive circular economy, taking into account the shocks and reverberations experienced by the economy as a whole caused by the COVID-19 and war in Ukraine. The results are presented as a global ranking of countries based on the social component of the global inclusive circular economy. They confirm the high value of this component in the integrated indicator, which validates the hypothesis that inclusiveness is a necessary aspect of the global circular economy. The research results identify the countries capable of offering the best management solutions to social disbalances and other weaknesses, as well as the countries in need of model examples to tackle these issues.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.016 |
| 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".