"Strange Flesh" in the City on the Hill: Early Massachusetts Sodomy Laws and Puritan Spiritual Anxiety, 1629-1699
Bibliographic record
Abstract
In his sermon at the execution of a convicted man, Puritan minister Samuel Danforth used the term "strange flesh" to describe the man's deeds, which in the present would be recognized as sodomy and bestiality.Danforth and other Puritan leaders took responsibility for the spiritual welfare of all the people in their community; sexual activities that they associated with God's enemies terrified them.Believing that their spiritual "city on a hill" was threatened, these leaders tried to deter such behavior not only through passionate sermons that railed against "strange flesh," but through explicit civil laws that mandated harsh penalties for those who persisted.This project focuses on the language in legal and religious texts used by magistrates in Massachusetts Bay from 1629 to 1699.It makes explicit the links connecting law, sex, and religion in this early period.By reading the religious and legal texts together, and paying close attention to the sodomy and bestiality statutes, I show how spiritual anxiety over "strange flesh" dictated legal policy regarding sexual activity.The Bay Colony leaders enacted specific legal statutes because they feared "God's Judgment" since some people in the community practiced the biblical "abomination" of "unnatural sex."My argument is that the conjunction of religious and legal texts created different groups of "other" within the community that established and reinforced the Puritan 'godliness' and their "city on the hill."Legal statutes, legal commentary, and religious commentary provide the main primary sources for this project.Massachusetts Bay lawmakers consolidated individual legislation against "buggery" and "sodomy" into colonial legal codes in the midseventeenth century, and English legal manuals describing "buggery" in great detail circulated in the Atlantic world during this time period.Further, Massachusetts Bay Puritan leaders relied heavily on particular passages in the King James Version of the Holy Bible, especially Leviticus and Romans.The appeal to biblical precedent in the creation of law distinguishes these colonial elites and reveals their preoccupation with sexual sin, God's wrath, and spiritual punishment.iv For Ruth and Deb.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".