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Record W2066675545 · doi:10.7202/1008982ar

American Cold War Policies and the Enewetakese: Community Displacement, Environmental Degradation, and Indigenous Resistance in the Marshall Islands

2012· article· en· W2066675545 on OpenAlexvenueno aff
Martha Smith-Norris

Bibliographic record

VenueJournal of the Canadian Historical Association · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousDamagesResistance (ecology)PoliticsPolitical scienceSecrecyLawAtollCold warDevelopment economicsPolitical economySociologyReefEconomics

Abstract

fetched live from OpenAlex

During the Cold War, the United States conducted 43 nuclear shots, 12 chemical explosions, and numerous missile tests on the Enewetak Atoll of the Marshall Islands. Based mainly on archival documents and congressional hearings, this case study focuses on the human and environmental consequences of these American policies. To begin with, the essay highlights U.S. interests and authority in the Marshalls. As the United Nations trustee of the region, the United States conducted these military experiments with a great deal of secrecy and little interference from the outside world. Secondly, the essay examines the significance of these tests for the Enewetakese and their removal to Ujelang, a nearby atoll, for more than three decades. Thirdly, the paper emphasizes the various forms of resistance practised by the Enewetakese. By utilizing a number of political and legal methods, this tiny indigenous community drew attention to their plight in Washington and the United Nations. Finally, the essay discusses the islanders’ attempts to gain compensation from the U.S. Congress for the profound damages caused by the testing program. To date, Washington has failed to provide enough funds to adequately restore the environment of Enewetak or to fully compensate the islanders for their losses.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.014
GPT teacher head0.232
Teacher spread0.219 · 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.

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

Citations2
Published2012
Admission routes1
Has abstractyes

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