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Record W2321786671 · doi:10.1386/vi.4.2.97_1

Resistance and intervention through a radical ethical aesthetic: The art of Gu Xiong

2015· article· en· W2321786671 on OpenAlexaffabout
Barbara Bickel, Gu Xiong, Rita L. Irwin, Kit Grauer, Ruth Beer

Bibliographic record

VenueVisual Inquiry · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsEmily Carr University of Art and DesignUniversity of British Columbia
Fundersnot available
KeywordsResistance (ecology)ImmigrationSubjectivityIdentity (music)SociologyAestheticsCensorshipGender studiesPoliticsArtPolitical scienceLawPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Abstract This article biographically describes the identity and artistic development of internationally known artist Gu Xiong. Stories of his life during the Cultural Revolution in China and his immigration experience into Canada are expressed and documented through his multiple roles in a community-engaged research study that explores issues of migration, identity, place, displacement, community and the changing nature of geography. Gu Xiong’s unique immigration experiences are contextualized within a mutual encounter of co-shaping between his artist-self and immigrant-self experience. As a cultural worker, created metaphors become the markers of his journey. Despite multiple censorship of his contemporary art and research in his birth country, his approach to creative resistance is a radical ethical aesthetic that unfolds with a neo-vitalist political subjectivity. The outcome is an imminent future vision of hope for a complex world that nourishes individual hybrid identities, interconnected cultures and free societies.

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)0.005
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.036
Scholarly communication0.0080.003
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.126
GPT teacher head0.463
Teacher spread0.337 · 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

Citations2
Published2015
Admission routes2
Has abstractyes

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