Bibliographic record
Abstract
This is the first book that engages with the history of diagrams in physical, evolutionary, and genetic anthropology. Since their establishment as scientific tools for classification in the eighteenth century, diagrams have been used to determine but also to deny kinship between human groups. In nineteenth-century craniometry, they were omnipresent in attempts to standardize measurements on skulls for hierarchical categorization. In particular the ’human family tree’ was central for evolutionary understandings of human diversity, being used on both sides of debates about whether humans constitute different species well into the twentieth century. With recent advances in (ancient) DNA analyses, the tree diagram has become more contested than ever―does human relatedness take the shape of a network? Are human individual genomes mosaics made up of different ancestries? Sommer examines the epistemic and political role of these visual representations in the history of ‘race’ as an anthropological category. How do such diagrams relate to imperial and (post-)colonial practices and ideologies but also to liberal and humanist concerns?
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".