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Record W2531864606 · doi:10.1108/ijccsm-04-2015-0038

Governance and climate variability in Chinchiná River, Colombia

2016· article· en· W2531864606 on OpenAlexaff
Erika Acevedo, Sandra Turbay, Margot Hurlbert, Martha Helena Barco, Kelly Johanna Lopez

Bibliographic record

VenueInternational Journal of Climate Change Strategies and Management · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCorporate governanceVulnerability (computing)Climate changeGovernment (linguistics)AgricultureEnvironmental resource managementAdaptive capacityPsychological resilienceOriginalityEnvironmental planningPolitical scienceGeographyBusinessQualitative researchSociologyEconomicsEcologySocial science

Abstract

fetched live from OpenAlex

Purpose This paper aims to assess whether governance processes that are taking place in the Chinchiná River basin, a coffee culture region in the Andean region of Colombia, are adaptive to climate variability and climate extremes. Design/methodology/approach A mixed research method was used by reviewing secondary research sources surrounding the institutional governance system of water governance and disaster response and semi-structured qualitative interviews were conducted with producers and members of organizations within the institutional governance system. Findings This study found that there is a low response to extreme events. Hopefully, the growing national awareness and activity in relation to climate change and disaster will improve response and be downscaled into these communities in the future. Although, some learning has occurred at the national government level and by agricultural producers who are adapting practices, to date no government institution has facilitated social learning taking into account conflict, power and tactics of domination. Originality/value This paper improves the understanding of the vulnerability of rural agricultural communities to shifts in climate variability. It also points out the importance of governance institutions in enhancing agricultural producer adaptive capacity.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2016
Admission routes1
Has abstractyes

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