Inert Resilience and Institutional Traps: Tackling Bureaucratic Inertias Towards Transformative Social Learning and Capacity Building for Local Climate Change Adaptation
Bibliographic record
Abstract
The institutional and political contexts of climate action matter. Planning and sustainability science have parallel interests in politics and institutions, particularly in institutional reforms that balance continuity and change. Our theorizing inert resilience highlights micro (individual) and meso (institutional) foundations of macro-state capacities for climate adaptation through social learning and transformative capacity building. Using survey, conversations, and participant observation in a Philippine case study, we discuss six inertia-inducing institutional traps shaping climate adaptation challenges in inert resilience contexts. Examining resource constraints, value conflicts, and colonial legacies influencing inertia, we propose pathways toward local capacity-building and social learning for climate adaptation.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".