Re-theorizing the collective action to address the climate change challenges: Towards resilient and inclusive agenda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it