White animals: racializing sheep and beavers in the Argentinian Tierra del Fuego
Bibliographic record
Abstract
In the summer of 1946, a landowning bourgeoisie organized the II Livestock Exhibition of Tierra del Fuego, and the Argentinian Navy filmed the introduction of twenty Canadian beavers in the region. Both events echoed power disputes between a military government seeking to nationalize lands and capitals and the European landowners whose privileges were threatened. The events show that landowners and state officers negotiated their interests by articulating Argentina’s white exceptionalism with animals and against racialized others. Interrogating the interspecies articulation of whiteness in Tierra del Fuego during the 1940s, I examine how sheep and beavers helped secure white privilege through land concentration, breeding, racial purification, nature modernization, and eugenic moralities. To answer these questions, I analyze documents and films from local and national archives. My analysis shows the entangled racialization of humans and animals and its effects, including the appropriation of the Fuegian and native identification categories by settlers and the state. This article demonstrates that ‘White Argentina’ is a project desiring to live not only among white citizens but also among white animals. More broadly, I argue that including animals in race and ethnicity studies can better explain the intersectional production of race inequalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".