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Record W2982068107 · doi:10.1002/9781119520627.ch10

The Human Right to Water

2019· other· en· W2982068107 on OpenAlexaff
Rhett B. Larson, Kelsey Leonard, Richard Rushforth

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHuman rightsPoliticsRight to developmentRight to foodProperty rightsPolitical scienceSustainabilityCultural rightsLaw and economicsExclusive rightRight to propertyEnvironmental ethicsLawInternational human rights lawSociologyIntellectual propertyFood securityGeography

Abstract

fetched live from OpenAlex

Just as water is essential for life, so it is essential for the exercise of all rights, including civil and political rights, social and cultural rights, as well as economic and property rights. Thus, water itself should arguably be a right. After all, what other right matters to a person dying of thirst or cholera? But the formulation, interpretation and implementation of such a right raises difficult questions. How much is enough? How clean is clean enough? How low a price is affordable without raising concerns of sustainability? The recognition of water as a human right requires careful co-development of a workable policy, from the legal, technical and political communities. This chapter provides perspectives from law, engineering and political science regarding the development and implementation of the human right to water. Part 1 provides the history and theoretical background on the legal development of the human right to water. Part 2 discusses the technical and economic challenges and promise of implementing the human right to water. Part 3 discusses the public policy, political and environmental challenges facing the human right to water and the future development of that right.

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)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.024
Scholarly communication0.0070.006
Open science0.0010.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0160.004

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 head0.007
GPT teacher head0.266
Teacher spread0.259 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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