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Record W1880461392 · doi:10.21971/p73g6b

Faith, Identity, and Nationalism: The Impact of the May Thirtieth Incident on China's Christian Colleges

2008· article· en· W1880461392 on OpenAlexvenueno aff
John Barwick

Bibliographic record

VenueCrossing boundaries · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsChinaFaithProtestantismPower (physics)Identity (music)NationalismPolitical scienceNexus (standard)BeijingPoliticsGender studiesReligious studiesHistoryMedia studiesSociologyTheologyLawArtAestheticsEngineeringPhilosophy

Abstract

fetched live from OpenAlex

The Christian colleges founded in China by Protestant missionaries in the early twentieth century constituted a major nexus of cultural exchange between East and West, but also raised complex issues of identity and power both for the missionaries and their students. The tragic killing of eleven student protesters in Shanghai by British troops in May of 1925, an event that came to be known as the May Thirtieth Incident, brought many of these tensions to the surface. Tin's paper examines the impact of this event on three of the Christian colleges—Yenching University in Beijing, St. John's University in Shanghai, and Lingnan University in Canton. The reaction of each school was different, reflecting not only the influence of geography and political factors, but the vision of mission education embraced by their respective leaders. In the end. however, none of the institutions were left untouched by the incident, which triggered a shift in lines of identity and power that favoured Chinese interests. The resulting changes at the colleges can be seen as a harbinger of a coming era in which Western imperial domination would meet a similar fate.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.020
Scholarly communication0.0060.002
Open science0.0010.007
Research integrity0.0010.003
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 head0.038
GPT teacher head0.339
Teacher spread0.300 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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