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Record W3045625817 · doi:10.7202/1070535ar

Anthologizing classical Chinese poetry in the twentieth and twenty-first centuries: poetics and ideology

2020· article· en· W3045625817 on OpenAlexvenueno aff
Wang Feng, Kelly Washbourne

Bibliographic record

VenueMeta Journal des traducteurs · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
FundersNational Office for Philosophy and Social Sciences
KeywordsSelection (genetic algorithm)IntertextualityPoetryIdeologyLinguisticsPoeticsLiteratureConversationFocus (optics)Representativeness heuristicClassical ChineseHistorySociologyComputer scienceArtPhilosophyPsychologyPolitical sciencePoliticsArtificial intelligenceSocial psychologyLaw

Abstract

fetched live from OpenAlex

We seek in this study to consider anthologizing as a decision-making activity, marshalling evidence from texts and paratexts reflecting anthologists’ poetic and ideological criteria for selection; to analyze the construction of a national literature accomplished through text selection and omission; and to provide evidence of the systematicity and intertextuality of anthologized texts as interrelated ecologies. We will focus largely on translation history decisions and procedures such as selection and the anthologists’ rationale; presentation format (monolingual, bilingual, the use of Chinese); provenance (direct or indirect translation); and target audience. In exploring criteria for selection, we intend to shed light on the complex problem of representativeness. We posit that anthologizing and its canon-forming and canon-redefining choices appear dialogically, in conversation with various kinds of previous texts, which serve as a kind of “original” to the anthologist’s “translation.” Our corpus includes multi-author anthologies of classic Chinese poetry that were published from the 1890s to the present.

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: Empirical
Teacher disagreement score0.937
Threshold uncertainty score0.512

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 studies0.0010.001
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 head0.072
GPT teacher head0.286
Teacher spread0.214 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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