Demographic Knowledge and Nation‐Building: The Peruvian Census of 1940
Bibliographic record
Abstract
Abstract Demografisches Wissen und ‘Nation‐Building’: Der peruanische Zensus von 1940. Die Demografen, die 1940 die peruanische Volkszählung organisierten, stellten die zunehmende ethnische Heterogenität Perus als Zeichen aufbrechender kultureller Grenzen und als Symbol einer tragfähigen peruanischen Identität dar. Diese besondere demografische Dynamik war ihrer Ansicht nach ein Motor der nationalen Entwicklung. Dieser Aufsatz analysiert die verschiedenen Formen, in denen Demografen kulturelle Heterogenität als einen potentiellen Vorteil des Landes konstruierten. Hierdurch wird deutlich, wie akademisches Wissen über ethnische Hybridität mit den nationalistischen Projekten der zweiten Hälfte des 20. Jahrhunderts verbunden war. Er analysiert weiterhin den Einfluss, den die ideologischen Sympathien der peruanischen Demografen für den Sozialismus auf ihre wissenschaftliche Arbeit hatte. Demographic Knowledge and Nation‐Building: The Peruvian Census of 1940. The demographers who organized the 1940 census of Peru portrayed the increasingly mixed‐race Peruvian population as indicative of the breaking down of cultural barriers to the emergence of a robust Peruvian identity, a process that, they claimed, would lead to greater national development. This paper analyzes the ways in which demographers constructed cultural heterogeneity as a potential national asset. This reveals how scientific knowledge of miscegenation affected the formation of a nationalist project in the second half of the twentieth century, and also how demographers' ideological commitments to socialism shaped scientific practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".