AI: A Key Enabler of Sustainable Development Goals, Part 1 [Industry Activities]
Bibliographic record
Abstract
We are witnessing a paradigm shift regarding how people purchase, access, consume, and utilize products and services as well as how companies operate, grow, and deal with challenges in a world that is continuously changing. This transformation is unpredictable thanks to fast-growing technological innovations. One of the cornerstones is artificial intelligence (AI). AI is probably the most rapidly expanding field of technology, due to the strong and increasingly diversified commercial revenue stream it has generated. The anticipated benefits and risks of the pervasive use of AI have encouraged politicians, economists, and policy makers to pay more attention to the results. Given the fact that AI's internal decisionmaking process is nontransparent, some experts consider it to be a significant existential risk to humanity, while other scholars argue for maximizing the technology's exploitation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".