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Record W4406930831 · doi:10.1080/10455752.2024.2448671

The Datafication of Environmental Injustice

2025· article· en· W4406930831 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCapitalism Nature Socialism · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersUniversity of WashingtonYork UniversityYale University
KeywordsInjusticePolitical scienceEnvironmental ethicsPhilosophyLaw

Abstract

fetched live from OpenAlex

This paper discusses the contradictory effects of geography and environmental justice research on state administrative processes. Drawing on the siting of a liquefied natural gas (LNG) plant on the Tideflats of Tacoma Washington, we argue that research that fails to consider the limitations of administrative violence becomes complicit in it. Through datafication, scientific research has repeatedly documented the harms of industrial development while taking the violence that made the Tideflats as a given. The Puyallup Tribe and environmental organizations’ lawsuit reveal the complicity of science in understanding landscape through a narrow political lens, ignoring the context of settler colonialism and the settler state’s responsibility to Indigenous nations. In this way, academic researchers facilitate administrative violence by participating in drawn out regulatory and legal processes while the environmental injustice in question continues.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.006
GPT teacher head0.315
Teacher spread0.309 · 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