Bibliographic record
Abstract
In February 2020, Miwa Yanagi: Myth Machines (2019-2020), a traveling exhibition of works by the Japanese contemporary artist Miwa Yanagi (1967-), concluded its tour across Japan after failing to incite meaningful critical response from art historians and critics. The year-long, five-museum itinerary of the solo show reflected the public’s keen interest in Yanagi’s first major exhibition in a decade, but the enthusiasm was betrayed by the paucity of scholarly attention; beyond the four essays included in the catalogue, hardly any scholar or critic seriously engaged with the artist who previously represented Japan at the Venice Biennale and whose work continues to be exhibited internationally. The few texts that appeared display a noticeable anxiety toward Myth Machines—in particular its unapologetic juxtaposition of photography and theater—which suggests a failure of the prevailing art historical language to speak and write about Yanagi’s career. In response to this laconic condition, this paper identifies the concept of heterotopia, delineated by Michel Foucault on three occasions between 1966 and 1967, as a useful device to activate a discourse on Yanagi’s exhibition. A reading of Myth Machines as a heterotopia reveals an exhibition that astutely comments on the ongoing global political moment defined by divisions along racial, gender and national boundaries, visually symbolized by former American president Donald Trump’s divisive border wall.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".