Generative Artifacts: Chinatown and an Ornamental Architecture of the Future
Bibliographic record
Abstract
This article proposes the term ‘generative artifact’ to define a new method of imagining the future, one derived from artistic and architectural interpretations of non-linear time, material exploration, and relationship building. This contrasts the imagining that happened in the past by European and North American dominant culture, born out of fears of a declining Western hegemony and resulting in socially constructed hierarchies based on race. To investigate this historic and outdated imagining of culture, we trace the history of Chinatown and the ornamented feminine body as a physical example of hypervisibility in the North American city. First, we examine the current discourse on Chinatowns’ Orientalist aesthetics, legitimacy through institutionalized nonspecificity, and architectural/artifactual heritage, which serve as a mirror and moor for the Chinese diaspora today. Here, we find clues on how to navigate and leverage the spectacle of the racial image, the continuous merging of person and thing, and the tropes that the racialized body might find itself answering for. To illustrate the potential of the generative process and through the lenses of Anne Anlin Cheng’s theory of ornamentalism and Legacy Russell’s glitch feminism, this article places Chinatown adjacent to the worldbuilding and artistic practices of seven contemporary artists and architects. This includes Astria Suparak (performance critique), Curry J. Hackett (AI, installation), Shellie Zhang (sculpture), Lan “Florence” Yee (textile), Debra Sparrow (weaving, murals), Thomas Cannell (sculpture), and the author (performance). All are from varied cultural backgrounds who create ‘generative artifacts’ in their creative practices—works that playfully slip between sign/icon, high/low tech, and authentic/invented culture to point towards a path to imagining more expansive futures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".