Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.012 | 0.045 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".