Historicizing Humans: Deep Time, Evolution, and Race in Nineteenth-Century British Sciences, ed. Efram Sera-Shriar
Bibliographic record
Abstract
Arising from, and surpassing, a 2015 special issue of Studies in History and Philosophy of Biological and Biomedical Sciences, this collection presents a wide-ranging series of case-studies in evolutionary thought in the Victorian British sciences. The primary focus is ‘deep time’—the exponentially expanded timeline of human prehistory opened up by geology, palaeontology and evolutionary biology—which brings to the fore the natural rather than the social sciences, although Victorian use of the ‘comparative method’ means that linguistics, and especially anthropology, are also well represented. A related context is empire, both formal and informal, and the sciences of race that it produced and which helped sustain it. ‘British’ in the subtitle is interpreted broadly, including not only British-born producers of knowledge working out in the empire, but also those who were products of the wider imperial world. A third emphasis is the complex nexus of science and religion. The collection commences in the Pleistocene with Chris Manias’ chapter on Victorian depictions of prehistoric humans’ relationships with ancient animals. The chapter is nicely illustrated with a series of vivid images from popular texts. Nanna Kaalund considers the Canadian naturalist John William Dawson, whose writings combined a monogenetic account of human origins with a ‘day-age’ theory of prehistoric time, in which the biblical seven days of Creation were each equated with a geological period. Whereas for Dawson science and religion were two sides of the same coin, the anthropologist E.B. Tylor, the subject of editor Efram Sera-Shriar’s chapter, employed a deep-time evolutionary theory of religion, treating it as a phenomenon explicable by scientific methods. Travel in Latin America was formative for Tylor, as also for the lesser-known English anthropologist William Bollaert, whose writings on the ‘Red Man’ are discussed in the fourth chapter by Maurizio Esposito and Abigail Nieves Delgado. Bollaert’s polygenist analysis divided humanity into several species, although with allowances for racial variations within each species as historical products of environmental influences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".