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Record W4381891784 · doi:10.1177/00220094231182443

Imperial Ruin and Military Waste on Johnston Atoll

2023· article· en· W4381891784 on OpenAlexaff
Rohini Patel

Bibliographic record

VenueJournal of Contemporary History · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAtollAgent OrangeScholarshipEnvironmental justiceLawPolitical scienceOceanography

Abstract

fetched live from OpenAlex

In 1970, the United States military was ordered to halt the use of herbicides in the Vietnam War. The suspension pre-empted a series of considerations by the military to determine what to do with the millions of gallons of surplus herbicides, such as Agent Orange, White, and Blue. In 1972, in response to a directive to return all Agent Orange stock to the continental United States for disposal, officials moved the herbicides to Johnston Atoll in the Pacific Ocean. In the 1980s, Johnston Atoll was slowly transformed into the site of the US military's first chemical munitions incineration facility. The military's use of the Atoll as a munitions waste site offers material and historical traces of the vast and continued global circulation of US military waste, how the military conceptualized the ‘destruction’ of such waste, and where it was deemed acceptable to house and carry out these attempts of waste removal. Drawing from primary sources including military scientific studies and correspondence, and situated within environmental justice and postcolonial science studies scholarship, I offer a reading of the Atoll as a place that was used to obscure, yet laid bare, several unattended histories and contemporalities of US empire.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.892

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.198
Teacher spread0.182 · 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 designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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