Reimagining Climate Futures: a Deliberative Dialogue on Circularity Part I
Bibliographic record
Abstract
As the world gathered in Sharm el-Sheikh, Egypt forCOP27, seven years after the historic Paris Agreement,climate observers noted that the climate crisis isunraveling faster than ever before. With devasting floodsand wildfires, communities are seeking innovativesolutions that can be scaled quickly, provide resilience andensure sustainability for a diverse range of lifeforms. Withgreenhouse gas (GHG) emissions continuing to climb, andthe latest geopolitical threats making for an ever morecomplex environment, the pace of transformation of all oursystems needs to quicken, all the while ensuring a just andequitable transition for all.Fragmentation, siloeing, disconnection and politicalpolarization affect a wide range of human experiencesand systems and add to the complexity of climatesolutions. Research and science have also resulted in verycompartmentalized disciplines where trans- multi- andinter-disciplinary research, even if more and more active, isstill not very well evaluated and funded. As we increasinglyrealize we operate in complex and interconnected adaptivesystems, researching one subject becomes difficult withoutconsidering all connected topics, and their dynamics.There is a strong invitation and a need for a cultural shiftin how we view human systems and their relations to theenvironment. As scholars and practitioners, we proposed to convenean interdisciplinary, intersectoral and intercultural groupof academics, urbanists, public servants, elected officialsand citizens to develop a framework for climate-readinessbased on a circular model approach.The circularity framework for climate readiness willenable communities to reconnect with hope and developinnovative collaborative ideas and worldviews togetherwith concrete proofs-of-concept, such as in the circular citymodel currently tested in Europe, and scale efforts to meetthe challenges of our changing climate in a relational andcommunity-oriented way.
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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 | 0.110 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.046 | 0.105 |
| Scholarly communication | 0.033 | 0.042 |
| Open science | 0.006 | 0.043 |
| Research integrity | 0.026 | 0.042 |
| Insufficient payload (model declined to judge) | 0.008 | 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".