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Record W591544950 · doi:10.33137/rr.v34i4.18661

The Origins of Racism in the West

2012· book· en· W591544950 on OpenAlexvenueno aff
Miriam Eliav‐Feldon, Benjamín Isaac, Joseph Ziegler, Sciltian Gastaldi

Bibliographic record

VenueRenaissance and Reformation · 2012
Typebook
Languageen
FieldSocial Sciences
TopicAfrican history and culture analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRacismPrejudice (legal term)JudaismModernityUniversalismPhysiognomyMiddle AgesProtestantismReligious studiesAntisemitismEarly modern EuropeHistoryRace (biology)EmpireClassicsArtLiteratureArt historyAncient historyPhilosophyTheologyAnthropologySociologyGender studiesArchaeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.265
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations32
Published2012
Admission routes1
Has abstractyes

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