Global Social Sustainability and Inclusion: The “Voice” of Social and Environmental Imbalances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it