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Record W2788744868 · doi:10.29173/mruhr464

"The Prevention of That Abominable Mixture and Spurious Issue": Continuity in the Prosecution of Miscegenation in Virginian Laws and Court Decisions, 1630-1691

2018· article· en· W2788744868 on OpenAlexvenueno aff
S. Lilley

Bibliographic record

VenueMount Royal Undergraduate Humanities Review (MRUHR) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationLawColonialismPolitical scienceCriminologySociologyHistory

Abstract

fetched live from OpenAlex

In the historical debate over the legitimation of Virginian black slavery in the seventeenth century, some historians argue that the legal prosecution of interracial sexual relations was a calculated effort to institute slavery. Conversely, others assert that lawmakers and law enforcers did not actively discourage interracial sex until they enacted legislation that explicitly forbade miscegenation in the 1660s and again in the 1690s. However, an examination of Virginian laws and court decisions regarding fornication from 1630 to 1691 reveals a different story. Colonial authorities inherited a revulsion towards miscegenation from the English intellectual and religious tradition, and they used three different legal methods to prevent sex between blacks and whites. Before they introduced legislation that explicitly sought to punish whites for miscegenation in the 1660s and 1690s, the secular authorities of the 1630s and 1640s resorted to enforcing moral laws originally meant for the Church courts, and they then introduced general laws against fornication that were disproportionately applied to cases of pre-marital sexual intercourse involving blacks. While the methods of sexually segregating white and black colonists changed over the course of the seventeenth century, the desire to prevent miscegenation was always present in the minds of colonial officials in this era.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.026
GPT teacher head0.310
Teacher spread0.284 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMount Royal Undergraduate Humanities Review (MRUHR)Same topicAmerican Constitutional Law and PoliticsFrench-language works237,207