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Record W2339750967 · doi:10.3167/hrrh.2015.410308

Noble Ghosts, Empty Graves, and Suppressed Traumas: The Heroic Tale of “Taiyuan's Five Hundred Martyrs†in the Chinese Civil War

2015· article· en· W2339750967 on OpenAlexvenueno aff
Dominic Meng-Hsuan Yang

Bibliographic record

VenueHistorical Reflections/Réflexions Historiques · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEPICNationalismAncient historyChinaCommunismPoliticsSpanish Civil WarState (computer science)HistoryDemocratizationNarrativeBattleEconomic historyPolitical scienceLawDemocracyArtLiterature

Abstract

fetched live from OpenAlex

On 19 February 1951, a state-sponsored funeral took place in north Taipei in which a splendid cenotaph to commemorate the “five hundred martyrs of Taiyuan”— heroic individuals who died defending a distant city in northern China against the Chinese Communist encirclement—was revealed. In the four decades that followed, the Nationalist government on Taiwan built a commemorative cult and a pedagogic enterprise centering on these figures. Yet, the martyrs' epic was a complete fiction, one used by Chiang Kai-shek's regime to erase the history of atrocities and mass displacement in the Chinese civil war. Following Taiwan's democratization in the 1990s, the repressed traumas returned in popular narratives; this recovery tore the hidden wounds wide open. By examining the tale of the five hundred martyrs as both history and metaphor, this article illustrates the importance of political forces in both suppressing and shaping traumatic memories in Taiwan.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.019
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0020.004
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 head0.043
GPT teacher head0.334
Teacher spread0.291 · 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
Published2015
Admission routes1
Has abstractyes

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