Bibliographic record
Abstract
This chapter examines the racial thinking of Vojtěch Suk (1879–1967), one of the most influential physical anthropologists in Central Europe. Suk had conducted several expeditions to collect data about ‘primitives’ on different continents. His research shaped not only arguments in favour of segregating Jews and Rusyns in the Czechoslovak periphery, but also an internationally accepted method for racialising minorities by presenting them as self-isolated ‘primitives’. The interrelation between Suk’s academic career and the trajectories of his public engagement is explored in the context of the colonial encounters that informed his views on whiteness. Three periods in Suk’s academic biography are explored through the layering of different conceptualisations regarding the interrelation between human evolution and race: (1) his engagement with physical anthropology (1905–13) and his first expeditions for measuring self-isolated ethnic groups on the periphery of the Austro-Hungarian empire; (2) his African expeditions under the supervision of the Smithsonian Institute (1913–21); (3) his Labrador and Ruthenian trips between 1921 and 1928. A visual analysis of the photographs taken by Suk to present the outputs of his research furthers our understanding of the connection between theoretical approaches towards racial thinking and their public impact. Suk’s career shows that investigating indigenous people was a way of overcoming the peripheral position of Central European scholars. It also confirmed the role of science as an agent of the global racial order that produced whiteness aligned with international and national priorities. The case of Suk reveals the complex process of creating and applying whiteness that began in the 1900s and continues to this day as an indispensable part of racialising ‘peripheral’ Europe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".