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Record W2020697945 · doi:10.1163/17087384-12342026

Integrated Water Resource Management, Public Participation and the ‘Rainbow Nation’

2014· article· en· W2020697945 on OpenAlexvenueno aff
Jona Razzaque, Eloise S. Kleingeld

Bibliographic record

VenueAfrican Journal of Legal Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated water resources managementStakeholderPublic participationSustainabilityPolitical scienceParticipatory developmentBusinessCitizen journalismHuman rightsWater resourcesEnvironmental resource managementEnvironmental planningPublic administrationPublic relationsLawEconomicsGeographyEcology

Abstract

fetched live from OpenAlex

Abstract This article provides varied examples of Integrated Water Resource Management (IWRM) and public participation interaction in South Africa. It critically examines the inadequate application of IWRM, and shows how the unbalanced interpretations of IWRM as well as a lack of good development practice and participatory rights manifest in negative outcomes for the poorest and most vulnerable. This paper, first, highlights that if decision-makers are primarily fixed on economic concerns, they induce inefficient IWRM framework that fails to balance water as a social, economic and ecological concern. Second: when the state fails to consult people and violate human and environmental rights, court battles ensue between the state and the people. These court cases are generally expensive for both sides and marred with delay. Third: positive outcomes can be attained through multi-stakeholder dialogue platforms which can operate as a sort of conflict resolution mechanism encompassing divergent views, but still offering beneficial outcomes. The frameworks and practical examples set by the Water Dialogues South Africa can facilitate public participation and capacity building if applied at local levels by decision-makers. IWRM with public participation at its heart engenders an ultimate objective for better water sustainability and water security in South Africa.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.059
GPT teacher head0.308
Teacher spread0.249 · 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 designNot applicable
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

Citations4
Published2014
Admission routes1
Has abstractyes

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