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Record W2062868106 · doi:10.5751/es-06885-190415

Political ecology of inter-basin water transfers in Turkish water governance

2014· article· en· W2062868106 on OpenAlexvenueno aff
Mine Işlar, Chad Boda

Bibliographic record

VenueEcology and Society · 2014
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governancePolitical ecologyPoliticsTurkishStructural basinEcologyEnvironmental governanceEnvironmental resource managementGeographyEnvironmental scienceWater resource managementPolitical scienceBusinessBiology

Abstract

fetched live from OpenAlex

We explore the emergence of two contemporary mega water projects in Turkey that are designed to meet the demands of the country's major urban centers.Moreover, we analyze how policy makers in the water sector frame problems and solutions.We argue that these projects represent a tendency to depoliticize water management and steer away from controversial issues of water allocation by emphasizing large-scale, centralized, technical, and supply-oriented solutions.In doing so, urgent concerns are ignored regarding unsustainable water use, impacts on rural livelihoods, and institutional shortcomings in the water sector.These aspirations build heavily on prevailing discourses of modernity, development, and economic growth, and how urban centers are perceived as drivers of this growth.In the light of these tendencies, social and environmental implications are downplayed, even though the projects will change or already have changed the dynamics within urban-rural life and agricultural water resources practices.We develop an understanding of how such projects are presented as the only solution to problems of water scarcity in Turkey.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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 designQualitative
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

Citations32
Published2014
Admission routes1
Has abstractyes

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