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Record W7113904103 · doi:10.5751/es-16781-300446

Hydrosocial power and territorial transformation: a case study of the Longaví River sub-basin in south-central Chile

2025· article· en· W7113904103 on OpenAlexvenueno aff

Bibliographic record

VenueEcology and Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
FundersCHIST-ERAFondo de Financiamiento de Centros de Investigación en Áreas PrioritariasCentro de Recursos Hídricos para la Agricultura y la MineríaAgencia Nacional de Investigación y DesarrolloAgenția Națională pentru Cercetare și Dezvoltare
KeywordsCorporate governanceSustainabilityPower (physics)Environmental governanceAgricultureResource (disambiguation)ColonialismInequality

Abstract

fetched live from OpenAlex

This article examines the configuration of hydrosocial territories (HST) and hydrosocial power (HSP) in the Longaví River sub-basin of south-central Chile. Drawing on land-use change analysis, documentary review, and interviews with key actors, we characterize how agricultural intensification and forestry expansion have reshaped both landscapes and water governance over the past three decades. The results show that HSP operates through infrastructure, discourses, and institutions that concentrate water rights and decision-making power in the hands of a few dominant actors. These dynamics reinforce historical and colonial configurations of HST, reproducing socio-environmental inequalities and marginalizing small-scale farmers and local communities. Such path-dependent processes not only constrain the emergence of alternative governance arrangements but also undermine the sustainability of water resource management amid increasing scarcity. By tracing socio-spatial dynamics, the study contributes to understanding how power asymmetries are embedded in hydrosocial relations and discusses their implications for advancing more equitable water governance in Chile and similar contexts.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.004
GPT teacher head0.235
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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

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