Note: Leveraging Artificial Intelligence to build a Data Catalog and support research on the Sustainable Development Goals
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs) are the framework adopted by the global community to encourage taking actions on the multiple challenges facing the world today to ensure environmental protection, health and well-being, and economic prosperity. This framework provides a detailed list of indicators that are interconnected and cover a holistic view on sustainable development. The goals were defined by the United Nations General Assembly in 2015 and expected to be achieved by 2030. Since the release of this agenda, the research community has begun to intensify work in these areas, yet these efforts seem to be relatively limited. This is especially true about the employment of data and artificial intelligence (AI), which are not widely engaged in SDGs related topics. The AI-based research on SDGs and further developments depends heavily on the availability and accessibility of related real-world data collected by the community. However, there is no central, structured, and holistic database of datasets and metadata associated with the SDGs, which prevents large-scale collaboration on these topics. In this paper, we present the SDG Data Catalog, a global open-source database indexing SDG-related datasets, associated metadata, and research networks. We describe the construction of this catalog, which relies on state-of-the-art natural language processing models with human supervision. The catalog breaks down data silos and helps sustainability researchers navigate the data sea to initiate effective collaborations.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.029 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".