MétaCan
Menu
Back to cohort
Record W3153751796 · doi:10.1080/17565529.2021.1904811

No room to manoeuvre: bringing together political ecology and resilience to understand community-based adaptation decision making

2021· article· en· W3153751796 on OpenAlexafffund
Laura Beckwith

Bibliographic record

VenueClimate and Development · 2021
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
FundersInternational Development Research Centre
KeywordsMaladaptationPoliticsAdaptation (eye)Psychological resiliencePolitical ecologyCommunity resiliencePower (physics)Perspective (graphical)Resilience (materials science)SociologyEnvironmental ethicsEnvironmental resource managementPolitical scienceEcologyPsychologySocial psychologyEconomicsComputer scienceBiology

Abstract

fetched live from OpenAlex

While community-led adaptation is an increasingly important subject in both academic and development circles, the politics behind adaptation decision making receives less attention. Specifically, an absence of adaptive actions is often understood as maladaptation. This study shows how incorporating lessons from political ecology such as an analysis of historically produced socio-political structures can add value to a resilience perspective by making clearer the contextual forces that shape adaptation decision making. Using the case study of urban farming in Phnom Penh, the political reality behind adaptation decision making is explored to reveal that the decision not to adapt may be a rational response to avoid risk in a situation of significant power imbalances. This understanding is important to inform the kind of policy measures and development interventions that are appropriate to support community-led 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.031
Scholarly communication0.0100.011
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.316
Teacher spread0.274 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueClimate and DevelopmentSame topicChild Nutrition and Water AccessFrench-language works237,207