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Record W4308200616 · doi:10.3390/rel13111062

Narratives of Religious Landscape: Reading Gender and Chinese Buddhism in the Travel Writing of Christian Women

2022· article· en· W4308200616 on OpenAlexaff
Anne Baycroft

Bibliographic record

VenueReligions · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBuddhismNarrativeChinaProtestantismReading (process)HistoriographyReligious studiesHistoryGender studiesLiteratureSociologyPhilosophyArtArchaeology

Abstract

fetched live from OpenAlex

This article explores the narrative descriptions of the Chinese religious landscape embedded within nineteenth century Christian missionary writings. I demonstrate the potential use of Protestant missionary writings as sources in the academic study of religion in China for both the physical descriptions of religious places that they contain and the narratives they express regarding the religious activities and identities of Chinese women. Of particular interest to this study are the religious encounters experienced between Christian and Buddhist women. My analysis of the travel writings of three Protestant women, Eliza Bridgeman (1805–1871), Helen Nevius (1833–1910), and Isabelle Williamson (d. 1886), illustrates that Chinese women were highly active within sacred spaces across China. This article contributes to discourses on the history of women and Chinese Buddhism, offers historiographical insights into the origins of Western academic studies of Buddhism in China, and provides alternate source material for information about religious continuity and change in early modern China.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0090.010
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.284
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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