Ghosts in the Delta: USAID and the historical geographies of Vietnam's ‘other’ war
Bibliographic record
Abstract
This paper will provide historical and geographical nuance to Eyal Weizman's concept of the ‘humanitarian present’ through an interrogation of the United States Agency for International Development's (USAID) entanglement in Cold War counterinsurgency. Specifically, it focuses on Cold War Vietnam, where USAID, through its Offices of Rural Affairs and Public Safety, spearheaded the ‘other’ war for rural ‘hearts and minds’ through two distinct, yet related, suites of spatial interventions. First, it sought to indirectly ‘conduct the conduct’ of the South Vietnamese people by providing technical assistance and commodity support to the Strategic Hamlet and Revolutionary Development programs. USAID's counterinsurgency programming, however, was not only traversed by a ‘will to improve’: it was also marked by a ‘will to police’. Here, I am specifically referring to the central role that USAID's Office of Public Safety played in helping the government of South Vietnam establish a functioning National Police whose ‘internal security’ mandate eventually encompassed both a biopolitics of population control as well as a necropolitics of neutralization. Over the course of this essay I will theorize these two tracks of counterinsurgency programming as the Janus faces of a broader ‘war–police’ nexus geared towards catalyzing the fabrication of a modern social order in the Vietnamese countryside.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".