Catalysing SDGs Achievement Through Community Engagement: A Case Study of the Dayalbagh Model
Bibliographic record
Abstract
Abstract The G20 countries face multifaceted challenges in their pursuit of the sustainable development goals (SDGs). Progress has plateaued due to global health crises such as the COVID-19 pandemic, geopolitical tensions, and uneven economic growth. It is imperative then to reevaluate the existing strategies aimed at achieving the SDGs. To achieve the SDGs in a timebound manner, this study recommends a shift towards holistic sustainability, integrating the inner dimension of sustainability comprising values, beliefs, attitudes, spiritual and intuitive consciousness, and conscientiousness along with the external dimension that includes environmental, social, and economic factors. The Dayalbagh community in India exemplifies this through the Sigma Six Qualities-Values-Attributes (Q-V-A) model, which embraces responsible production and consumption through six elements: agriculture and dairy, education and healthcare, air quality, water quality, innovation, and human values. The model facilitates the transition towards holistic sustainability that encompasses the principles of Lifestyle for Environment (LiFE). The G20's role is vital in fostering holistic sustainability through a community-centric approach that promotes responsible production and consumption, fosters innovation, advocates sustainable agriculture, prioritises education and healthcare, and enhances community engagement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".