Potential Benefits and Dangers of Using Large Language Models for Advancing Sustainability Science and Communication
Bibliographic record
Abstract
Advancements in large language models (LLMs) provide opportunities to accelerate progress towards the attainment of the Sustainable Development Goals (SDGs). Current research largely overlooks the nuanced benefits and dangers LLMs introduce to sustainability research and communication, as well as broader challenges that need to be addressed in the longer term. This paper overcomes these shortcomings by introducing and discussing a framework that highlights how LLMs can benefit knowledge production, mobilization, and communication in the sustainability sciences, as well as any associated dangers. In addition, it outlines potential long-term challenges that must be acknowledged and addressed to ensure the responsible use of LLMs in advancing sustainability science. A key to the development and use of LLMs for sustainability science is the development of regulatory measures. These measures should be guided by what is needed for expanding sustainability science on the one hand and a holistic view to ensure its responsible use on the other. Failure to reflect and act on this might result in unintended consequences or misuse, making the technology another roadblock to progress towards the SDGs.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| 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".