The Origins of Racism in the West
Bibliographic record
Abstract
1. Introduction Benjamin Isaac, Joseph Ziegler and Miriam Eliav-Feldon 2. Racism: a rationalization of prejudice in Greece and Rome Benjamin Isaac 3. The invention of Persia in classical Athens H. A. Shapiro 4. Racism, color symbolism, and color prejudice David Goldenberg 5. Early Christian universalism and modern forms of racism Denise Kimber Buell 6. Illustrating ethnicity in the Middle Ages Robert Bartlett 7. Proto-racial thought in medieval science Peter Biller 8. Physiognomy, science, and proto-racism 1200-1500 Joseph Ziegler 9. Noble dogs, noble blood: the invention of the concept of race in the late Middle Ages Charles de Miramon 10. The carnal knowing of a coloured body. Sleeping with Arabs and Blacks in the European imagination, 1300-1550 Valentin Groebner 11. Was there race before modernity? The example of 'Jewish' blood in late-medieval Spain David Nirenberg 12. Religion and race: Protestant and Catholic discourses on Jewish conversions in the sixteenth and seventeenth centuries Ronnie Po-chia Hsia 13. Vagrants or vermin? Attitudes towards Gypsies in Early-Modern Europe Miriam Eliav-Feldon 14. The peopling of the New World: ethnos, race and empire in the Early-Modern world Anthony Pagden 15. Demons, stars, and the imagination: the Early-Modern body in the Tropics Jorge Canizares-Esguerra.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".