Book review: Bones and Ochre: The Curious Afterlife of the Red Lady of Paviland. Marianne Sommer. Cambridge, MA: Harvard University Press, 2007, xii + 398 pp, list of archives consulted, 14 figures, 2 appendices.
Bibliographic record
Abstract
In Bones and Ochre, author Marianne Sommer, a historian of science, aims to address her discipline’s neglect of paleoanthropology and prehistoric archeology (11). Sommer situates her book among other recent works in the history of science, such as Keller (2000), Secord (2000), and Daston (2000; 2004), as well as those which contextualize the anthropological sciences, including Hammond (1980), Bowler (1986), and Delisle (2007). Drawing on both published and archival sources, Sommer takes on the large task of tracing the history of paleoanthropology through the nineteenth and twentieth centuries as she follows the changing biography of the “Red Lady of Paviland.” She uses this ochre-stained fossil skeleton and its role (along with that of associated artifacts) as an “anthropological object,” at once a natural, material object and meaningful concept (6), to demonstrate the historically contingent nature of anthropological interpretation, as the Red Lady’s age, sex, ethnicity, and place in human history shift multiple times from discovery in 1823 to the present day.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.019 |
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".