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Record W3126994550 · doi:10.1080/14649357.2021.1875029

Inert Resilience and Institutional Traps: Tackling Bureaucratic Inertias Towards Transformative Social Learning and Capacity Building for Local Climate Change Adaptation

2021· article· en· W3126994550 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePlanning Theory & Practice · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTransformative learningBureaucracyPsychological resilienceSocial learningPolitical scienceClimate changePoliticsLegitimationAdaptive capacitySociologyCapacity buildingResilience (materials science)Adaptation (eye)Environmental resource managementPsychologySocial psychologyEconomics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.313
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it