Leveraging Digital Disruptions for a Climate-Safe and Equitable World: The Dˆ2S Agenda: [Commentary]
Bibliographic record
Abstract
A new report, Digital Disruptions for Sustainability Agenda (the DA2S Agenda), developed by Future Earth's Sustainability in the Digital Age initiative is discussed in this paper. The DA2S Agenda was developed over the course of a year, engaging over 250 experts from around the world through workshops, online consultations, and desktop research. This article provides an overview of the analysis and findings outlined in the DA2S Agenda. We begin with an overview of the research on how to change systems and drive societal transformations. We then describe the process used to develop the DA2S Agenda and provide a summary of the research and innovations outlined in it. The final section outlines near-term actions needed to establish the enabling conditions to drive the transformative systems changes needed for a climate-safe and equitable world.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.053 | 0.044 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".