Connecting climate science and society: reflections from early and mid-career researchers at the World Climate Research Programme Open Science Conference 2023
Bibliographic record
Abstract
This paper reflects the discussions of early and mid-career researchers (EMCRs) during the World Climate Research Programme Open Science Conference 2023 EMCRs Symposium, to advance climate knowledge for greater transformative power in society and impact on policy-making. These discussions focused on three key priority challenges: how to produce robust, usable, and used climate information at the local scale; how to address key climate research and knowledge gaps in the Global South; and how EMCRs could support policy-making with climate information. We present here our perspective on these major challenges, possible ways to address them, and what could be the contribution of EMCRs. In addition, we provide recommendations for actions that could be taken at the international and national levels to increase the voice and leadership of Global South researchers and EMCRs in international scientific endeavors. These recommendations might facilitate the integration of new technological tools or innovative approaches, promote interdisciplinary collaboration, and foster connections with local communities. This coordinated approach to international, regional and local initiatives will catalyze the process for urgent action on the environmental crisis before us.
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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 | 0.101 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.038 |
| Scholarly communication | 0.037 | 0.025 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.023 | 0.053 |
| Insufficient payload (model declined to judge) | 0.006 | 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".