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Record W4406315403 · doi:10.1558/bar.27645

Midwives as advocates

2025· article· en· W4406315403 on OpenAlexaff
Sara Verskin

Bibliographic record

VenueBody and Religion · 2025
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSociologyPolitical science

Abstract

fetched live from OpenAlex

Throughout the medieval world, women’s bodily statuses could have religio-legal consequences, and for that reason female midwives were sometimes called in as expert witnesses in Islamic legal proceedings to examine and testify about women’s bodies. Midwives in such roles appear, at first glance, to be the eyes and instruments of male authorities. Drawing on fatwas from North Africa and Islamic Spain, however, this article argues that the role of midwives was sometimes more complex. Each of the three cases presented here addresses a different concern about a woman’s body. In the first, a question arises as to whether a widow is pregnant. In the second, whether an orphan girl is sufficiently physically developed to marry. In the third, the question is whether and when a kidnapped and assaulted girl lost her virginity. In each case, the midwives repeatedly changed their testimonies. This was not a symptom of either dysfunction or unreliability; rather; it was a product of the midwives’ roles as agents of women in precarious positions, women whose calculations about what constituted their best interests were constantly shifting. The testimonies of midwives in these cases served less as a patriarchal means of weaponizing women’s own bodies against them, and more as tools for converting women’s personal distresses and hopes into actionable legal petitions. The midwives worked to secure the religio-legal status that would confer the greatest benefit on the women in need, and that would provide a framework for the community to meet those needs. Thus, it behooves historians to view these midwives not merely as instruments of prosecutorial and censorious men, but rather as advocates for women.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.310
Teacher spread0.299 · 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
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
Published2025
Admission routes1
Has abstractyes

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