A Quantitative Nexus Approach to Analyze the Interlinkages across the Sustainable Development Goals
Bibliographic record
Abstract
The 2030 Agenda for sustainable development comprises 17 Sustainable Development Goals (SDGs) that aimed at achieving universal access to basic needs and services for all. Moreover, the SDGs present a broad and comprehensive set of goals that cover social, economic and environmental aspects. The global SDGs are interlinked and they are either mutually supportive or conflicting. Informing about the interlinkages enables policy makers to harness synergies and manage any potential conflicts between policies engaged to achieve the SDGs. This paper introduces a framework to analyze interlinkages across the SDGs based on a bottom-up process which is supported by a quantitative nexus theoretical method to evaluate the direct and indirect quantitative interactions among SDG variables. Firstly, the general concept of analyzing interactions based on a bottom-up process is presented. Secondly, a quantitative nexus method based on input-output theory that permits the evaluation of the direct and indirect interaction effects among SDG variables is introduced. Lastly, a numerical experiment is presented and results are discussed.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".