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Record W2332955530 · doi:10.1177/1532708616640012

Hoover Damn

2016· article· en· W2332955530 on OpenAlexafffund
Jane Griffith

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaU.S. Department of Justice
KeywordsIndigenousSettlement (finance)White (mutation)ColonialismNewspaperLawHistoryPolitical scienceSociologyEnvironmental ethics

Abstract

fetched live from OpenAlex

Hoover Dam is a settler-colonial project, requiring Indigenous land and waterways while producing energy that enables further non-Indigenous settlement. In addition to the Dam’s engineering feats, its cultural production—art, pageantry, commemoration, and media—helped to buttress these claims to land. In this article, I offer the concept of dam/ning: how tactics used to preserve White settler memory, history, and claims to land and water seemingly appear to affirm Black and Indigenous lives but in fact veil violence. Also embedded in the term is damning: the strategies used to resist settler-colonial violence, dehumanization, displacement, and land theft. Dam/ning analyzes whose land these actions take place on, who claims this land and how, and what techniques people have used to resist. I draw from a tripartite archive: personal letters from Hoover Dam’s official artist (1920s-1940s), the Bureau of Reclamation’s magazine (1930s), and the town site’s local newspaper (1979). This article begins by establishing the practices of damming—the physical and cultural practices that enabled White settlement, which denigrated Indigenous and Black peoples while requiring their knowledge, art, and bodies; the second half of the article establishes the practices of damning, exposing ways Indigenous and Black communities fought these settler-colonial practices throughout the 20th century.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.003
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 head0.181
GPT teacher head0.497
Teacher spread0.316 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations4
Published2016
Admission routes2
Has abstractyes

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