World scientists’ warnings into action, local to global
Bibliographic record
Abstract
‘We have kicked the can down the road once again – but we are running out of road.’ – Rachel Kyte, Dean of Fletcher School at Tufts University. We, in our capacities as scientists, economists, governance and policy specialists, are shifting from warnings to guidance for action before there is no more ‘road.’ The science is clear and irrefutable; humanity is in advanced ecological overshoot. Our overexploitation of resources exceeds ecosystems’ capacity to provide them or to absorb our waste. Society has failed to meet clearly stated goals of the UN Framework Convention on Climate Change. Civilization faces an epochal crossroads, but with potentially much better, wiser outcomes if we act now. What are the concrete and transformative actions by which we can turn away from the abyss? In this paper we forcefully recommend priority actions and resource allocation to avert the worst of the climate and nature emergencies, two of the most pressing symptoms of overshoot, and lead society into a future of greater wellbeing and wisdom. Humanity has begun the social, economic, political and technological initiatives needed for this transformation. Now, massive upscaling and acceleration of these actions and collaborations are essential before irreversible tipping points are crossed in the coming decade. We still can overcome significant societal, political and economic barriers of our own making. Previously, we identified six core areas for urgent global action – energy, pollutants, nature, food systems, population stabilization and economic goals. Here we identify an indicative, systemic and time-limited framework for priority actions for policy, planning and management at multiple scales from household to global. We broadly follow the ‘Reduce-Remove-Repair’ approach to rapid action. To guide decision makers, planners, managers, and budgeters, we cite some of the many experiments, mechanisms and resources in order to facilitate rapid global adoption of effective solutions. Our biggest challenges are not technical, but social, economic, political and behavioral. To have hope of success, we must accelerate collaborative actions across scales, in different cultures and governance systems, while maintaining adequate social, economic and political stability. Effective and timely actions are still achievable on many, though not all fronts. Such change will mean the difference for billions of children and adults, hundreds of thousands of species, health of many ecosystems, and will determine our common future.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.018 | 0.029 |
| Insufficient payload (model declined to judge) | 0.032 | 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".