ARTIFICIAL INTELLIGENCE, LAW AND THE 2030 AGENDA FOR SUSTAINABLE DEVELOPMENT
Bibliographic record
Abstract
This paper has the following research problem: how can Artificial Intelligence (AI) contribute to the achievement of the goals of Agenda 2030 for Sustainable Development? In order to satisfy the problem, the first section aims to address the relationship between AI and SDG. Among the objectives that can be most influenced by technologies, both positively and negatively, are the SDG's that have water, health, agriculture, and education as their guideline. This approach will be achieved through the description and demonstration of reports provided by the United Nations Educational, Scientific, and Cultural Organization (UNESCO). The second section of the report criticizes the reduction or eradication of adverse effects that AI can have on society. A case study from countries such as China, the United Kingdom, and Canada is used as a guideline since they have a strong influence on the scenario addressed. To this end, deductive and integrated research methods are used, as well as the technique of case study research. In the end, it is shown that AI is an essential factor in the equation posed by Agenda 2030, provided it is duly observed and regulated. Bibliographical research and the integrated research method will be used
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".