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Record W7135003766

Bound by Margins, Rested in Rivers: How artists write, visualize, perform, practice and theorize community-based artworks

2025· other· en· W7135003766 on OpenAlexaboutno aff
Zi Wang

Bibliographic record

VenueYorkSpace (York University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLegibilityPower (physics)Openness to experienceStorytellingMuseologyWeavingStyle (visual arts)Diaspora
DOInot available

Abstract

fetched live from OpenAlex

This paper accompanies my MFA thesis show, Bounded by Margins, Resting in Rivers, exhibited from April 16–25, 2025, at Gales Gallery, York University. It encapsulates an inquiry into how artists write, visualize, perform, practice, and theorize community-based artworks. Since 2022, I, alongside my mother, artist Zhu Dandan, have engaged with Ontario’s diverse neighborhoods, Richmond Hill, Scarborough and Toronto, working with 90 community members, primarily visible minorities, and immigrants, through our shared workshop series, Project Cocoon. Our workshops employ an object-biography approach, harnessing visual, audio, and collaborative art-making to amplify memory-making. The thesis show, featuring mixed-media installations, artist books, prints, and performances, embodies these narratives, while this paper articulates their conceptual and ethical underpinnings. Facilitation, I argue, is not neutral; it navigates power dynamics, institutional constraints, and ethical tensions. Rooted in the diaspora context, my practice prioritizes care, dialogue, and openness to the unknown, fostering a relational aesthetic that resists dominant frameworks. Together, the show and paper explore how community-based art gains legibility and integrity, weaving a living collection of shared resilience that bridges personal and collective experience.

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)0.004
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.035
Scholarly communication0.0160.007
Open science0.0020.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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