“I Love Working for Uncle Sam, Lets Me Know Just Who I Am”: Culture, the Human Terrain System, and the Inquiry of World War I
Bibliographic record
Abstract
Abstract: In 2006, twenty-five teams of cultural anthropologists were deployed by the US military as part of the Human Terrain System (HTS). The backlash against the use of academics and the “militarization of culture” was immediate. The American Anthropology Association published a series of works admonishing HTS participants and, in a related move, lifted an almost century-old censure of Franz Boas’s 1919 work, “Scientists as Spies.” Boas’s once-marginalized argument—that the “scientist who uses his research as a cover for political spying forfeits the right to be classified as a scientist”—was now recast as an accepted professional principle. This paper explores these two interrelated instances of the contestation of culture in geopolitical military strategies. Placing Boas within a larger history of the use of and conflict over the definition of culture during World War I, I argue that anthropologist-spies were only the tip of the iceberg in his time, just as HTS is in ours. The Paris Peace Talks (1917–9), for example, saw social scientists working with the state on a massive scale to literally redraw the world map. This article focuses on one secretive American organization, the Inquiry, which worked closely with military and state apparatuses, gathering ethnographic/cultural data that helped carve out official state positions and war aims. By situating the HTS as part of this larger historical narrative, we see how the “ethnographic” culture concept came to occupy such a dominant (albeit contested) place in thought.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.044 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".