Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.009 |
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".