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Record W4205113134 · doi:10.24043/isj.378

Utopian and insular spaces in Chinese literature: An island approach

2022· article· en· W4205113134 on OpenAlexvenueno aff
Gang Hong

Bibliographic record

VenueIsland Studies Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
FundersNational Office for Philosophy and Social Sciences
KeywordsAlterityUtopiaDepictionConstructiveRelation (database)ChinaHistoryAestheticsThe ImaginaryReading (process)SociologyLiteratureEpistemologyPhilosophyArtArchaeologyArt historyLinguisticsPsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

In response to the relative lack of scholarly attention paid to the relationship between island utopia and Chinese literature, this paper studies the imagination of both island and insular geographies in Chinese ‘utopian’ literature using an island-sensitive approach. Employing an expanded and constructive conception of the island, the paper examines the heterogeneity of Chinese island and insular imaginaries in literary works from diverse historical periods, especially in relation to the dominant western model of the remote tropical oceanic island. Based on the finding that the alterity of Chinese island and insular imagination lies as much in its depiction of spatial ambiguities as in its mixing of diverse figures, I reflect further on the benefits and perils of adopting a west-inflected island approach in examining the imaginary landscapes of utopianism and insularity in Chinese literature. It is argued that Chinese island literature is more a reading effect enabled by an imported theoretical approach than any inherent tradition in itself. In the end, two paths for innovating island aesthetics and epistemologies in cross-cultural contexts are proposed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0050.017
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0000.001
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 head0.021
GPT teacher head0.315
Teacher spread0.294 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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