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Record W4416182302 · doi:10.1177/25148486251394605

Living space and the struggle against geothermal energy projects in Flores, Indonesia

2025· article· en· W4416182302 on OpenAlexaff
Emily T. Yeh, Cypri Jehan Paju Dale, Gregorius Afioma, Shae Frydenlund

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsUniversity of Toronto
FundersSocial Science Research Council
KeywordsArticulation (sociology)IndigenousGovernment (linguistics)Opposition (politics)Meaning (existential)Space (punctuation)

Abstract

fetched live from OpenAlex

Framed as part of the country's green energy transition, the Indonesian government declared Flores a “Geothermal Island” in 2017. However, geothermal projects have provoked strong opposition due to concerns about risks of water and air pollution, lethal accidents, and loss of livelihoods. This article examines how two Manggarai communities on Flores have struggled against geothermal development by mobilizing around the defense of ruang hidup or “living space,” a term that expresses the inseparable unity of homes, fields, water sources, and ritual sites - that is, of land, community, and ancestors. Although indigeneity is a contested category in Indonesia, these communities have strategically positioned themselves as indigenous through their articulation of ruang hidup in relation to the principle of Free, Prior, and Informed Consent (FPIC). These articulations, rooted in local practices of dwelling and place-making, and expressed through acts of translation that enable the scaling up of their claims, have led to temporary halts in projects funded by the World Bank and German Development Bank. The article contributes to geographies of indigeneity and decarbonization by showing how the contingent articulation and translations of living space have reshaped the meaning and implementation of geothermal energy projects.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.271

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 head0.005
GPT teacher head0.177
Teacher spread0.172 · 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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