Optimization and Forecasting Modelling to Analyse India’s Pursuit of the Sustainable Development Goals in Agenda 2030
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs), set in Agenda 2030, are examined in this study, along with India’s progress towards attaining them, and creative solutions based on forecasting and optimization modelling are presented. We investigate the complex alternatives between economic development, mainly focusing on GDP, sustainability—environmental concerns—and employment—a problem at the core of India’s sustainable development. We examine India’s development across several sectors like agriculture, mining, trades, construction, and so on, using a lexicographic goal programming framework, developing a hierarchical structure with four different levels and prioritizing the most important goal. Decisions are made from the highest priority level to the lowest priority level. Research goes beyond assessment by providing practical solutions to problems. A numerical study highlight the applicability of our strategy. By emphasizing the relevance of coordinating progress across decision-making levels for a more equal, prosperous, and sustainable future by 2030, this research delivers customized, context-aware solutions to accelerate India’s achievement of the SDG goals.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".