MétaCan
Menu
Back to cohort
Record W4313682850 · doi:10.19088/1968-2023.107

Environmental Policy Reform and Water Grabbing in an Agricultural Frontier in the Brazilian Cerrado

2023· article· en· W4313682850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIDS Bulletin · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLand grabbingAgricultureAppropriationDeforestation (computer science)Natural resource economicsWater resourcesGrassrootsGeographyWater resource managementEnvironmental protectionEnvironmental sciencePolitical scienceEconomicsEcologyPolitics

Abstract

fetched live from OpenAlex

The spread of soy monoculture in the Brazilian Cerrado relies on land and water grabbing, although water appropriation is a least studied issue in the current literature. A mixed-methods approach was used to study changes in water use in western Bahia and the evolution of water and environmental standards over the last 20 years. The results show that the deregulation of environmental laws by the Bahia state Institute for the Environment and Water Resources (Instituto do Meio Ambiente e Recursos Hidricos, INEMA) has facilitated deforestation and water grabbing for large-scale irrigation by industrial agriculture. The social dynamics of struggles and resistance to this process was also analysed. The results show that water appropriation in the neoliberal agricultural frontiers of the Cerrado has changed not only water use and flows but also water governance systems, flows of power, and the representations that underpin them.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.310

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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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