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Record W2901411707 · doi:10.5539/res.v10n4p164

Brazil’s and Scotland’s Water Policies: A North-South Comparison

2018· article· en· W2901411707 on OpenAlexvenueno aff
André Geraldo Berezuk, Antônio Augusto Rossotto Ioris

Bibliographic record

VenueReview of European Studies · 2018
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of Edinburgh
KeywordsPoliticsContext (archaeology)Political scienceNational PolicyRegional scienceWater qualityPublic policyPublic administrationEconomic growthGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

Water management is a main public policy issue and an important matter inside of the political context of any nation. The comprehension of water policies is directly related to national development strategies. This paper examines the water policies aspects of two different but emblematic national experiences (in Brazil and Scotland) and address multidimensional and territorialized questions. Brazil has the largest stock of surface freshwater in the world and the country’s development increasingly depends on adequate water policies and improved technical and managerial strategies. By its turn, Scotland is famous for high water quality and for recently implemented of the most ambitious institutional water mechanism in Europe. Our analysis contrasts the two national water policy frameworks through a consideration of their political and territorial particularities. This comparative analysis is undertaken by the use of common matrice that helps to showing the outcomes of each country policy. The text contributes towards the international debate on water institutional reforms and their associated political-hydrological challenges.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.030
GPT teacher head0.278
Teacher spread0.247 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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