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Record W4393357481 · doi:10.3390/rel15040437

Walking in “Masses and Elites”: Investigation into Donald MacGillivray’s Missionary Strategies in China (1888–1930)

2024· article· en· W4393357481 on OpenAlexaboutno aff
Yanhua Song, Zhao Wei, Doucheng Ma, Shulin Tan

Bibliographic record

VenueReligions · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
FundersMinistry of Water Resources
KeywordsChinaHistoryPolitical scienceEconomic geographyGeographyMedia studiesSociologyArchaeology

Abstract

fetched live from OpenAlex

From his arrival in China in 1888 to his departure in 1930, Canadian missionary, Donald MacGillivray (季理斐), was in China for more than 40 years. According to changes in the Chinese missionary situation, the key target of his mission was frequently adjusted. From his initial work in the early days in North Honan, to his work with officials and intellectuals in Shanghai in the late Qing Dynasty, then to students, women, and children in the Republic of China, Donald MacGillivray continued to preach to both the masses and the elites. His approach was flexible, ranging from oral preaching to academic publications. Relying on his interpersonal network, MacGillivray paid close attention to the social changes occurring in modern China. An evaluation of his activities in China can not only reveal the impact of individual missionaries in the process of Western learning and the transformation of Chinese knowledge in modern times, but also provide insight into the integration of Christianity into the indigenization process of 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)0.003
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 score0.337
Threshold uncertainty score0.671

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0170.010
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.302
Teacher spread0.283 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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