Re-theorizing the collective action to address the climate change challenges: Towards resilient and inclusive agenda
Bibliographic record
Abstract
Climate change poses a significant risk threatening the livelihood of people, communities, and cities worldwide. The stakes cannot be reduced to zero, so there is a constant need to re-theorize the collective action to address the climate change challenges. Doing so requires planning to reduce vulnerability to climate change. One of the most crucial challenges facing scientists, academics, citizens, and policymakers today is whether the collaborative, inclusive, and resilient climate change action can be implemented, assessed, and achieved. To respond to this question, this research aims to re-theorize, de-conceptualize, and analyze the collective effort to address the climate change challenges. First, the paper conceptualizes climate change resiliency as the ability to anticipate, prepare for, and respond effectively to climate-related risks, hazards, and threats. The existing challenges toward implementing resilient and inclusive climate change action have been analyzed. The paper theorizes the urban commons and collaborative governance to theorize collective efforts. This article concludes by identifying some critical determinants for the up‐scaling of collective action to address the climate change challenges. It can be supposed that any future inclusive and resilient collective action to address climate change is based on social learning to support decision-making, emphasizing inclusion and equity, which came in line with the United Nation’s 2030 SDGs.
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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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".