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Record W4306168697 · doi:10.5539/ach.v14n2p57

Binary Opposition and Gender Representation in The Tale of the Heike

2022· article· en· W4306168697 on OpenAlexvenueno aff
Hebatalla Omar

Bibliographic record

VenueAsian Culture and History · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClanBinary oppositionOpposition (politics)Representation (politics)NarrativeEmperorPoliticsHistorySemioticsLiteratureSociologyArtAnthropologyPhilosophyLawAncient historyPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Gunki monogatari (war tales) reflected the state of Japan during medieval times. An example of such stories, The Tale of the Heike (Note 1) describes the time surrounding the destruction of the Taira clan, illustrating how those events shifted the history of Japan. Concepts central to the narrative, including “mujōkan 無常観” and “hōganbiiki判官贔屓,” remain rooted in modern Japanese society. However, gender-oriented research on The Tale of the Heike is still limited. By applying semiotic analysis along with socio-historical approach, this study discusses the societal position of The Tale of the Heike in Japan, drawing attention to the female characters represented and analyzing the nature of gender in the story. This research also considers the historical and social backgrounds that produced the foundations for that gender representation. The study reveals that the representation of female characters, especially shirabyōshi (Note 2), had a deep political role. It also recognizes that Cloistered Emperor Go-Shirakawa involved many women in his approach to fighting the Heike clan. Finally, it demonstrates how binary opposition and gender representation in The Tale of the Heike may have been used to promote the Heike clan stereotypes, resembling certain forms of modern-day media.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.300

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 head0.031
GPT teacher head0.234
Teacher spread0.203 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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