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Record W2468793547 · doi:10.1002/9781118451694.ch9

Climate adaptation governance – theory, concepts, and praxis in cities and regions. The role of climate and water governance in supporting climate change adaptation processes

2016· other· en· W2468793547 on OpenAlexaffabout
Walter Leal Filho, Margot Hurlbert, Harry Diaz

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCorporate governanceClimate changeAdaptation (eye)Context (archaeology)Adaptive capacityFlexibility (engineering)Environmental resource managementEnvironmental planningMulti-level governancePolitical economy of climate changeClimate governancePolitical scienceEconomic systemBusinessEconomicsGeographyEcology

Abstract

fetched live from OpenAlex

The impacts of climate change have become very apparent and are increasingly becoming problematic for governments, communities, and individuals. The impacts of climate change on water and resulting crisis in relation to water resources has been characterized as a crisis of governance. This chapter argues that effective climate change governance must be poly-centric, allowing decision-making by several independent centers and actors, as well as adaptive, embracing design principles promoting flexibility. First, it provides a brief context if climate governance, and then discusses the role of law and legal mechanisms, specifically the property interest in water and how this institution can improve the adaptive capacity of rural agricultural producers. The chapter then explains policies surrounding water with their important contribution to agricultural producer adaptive capacity. It uses examples of the European and Canadian contexts to illuminate some discussion points and features of polycentric and adaptive governance.

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)0.001
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.047
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0040.024
Scholarly communication0.0100.006
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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
GenreOther

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

Citations1
Published2016
Admission routes2
Has abstractyes

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