Bibliographic record
Abstract
Introduction:Art Mecho Frenchy Lunning (bio) and Thomas LaMarre (bio) Art Mecho. Apparently an encounter of Art Deco with mecha, but much, much more; an embrace of the false image of luxury, yes, but piloted like a bipedal robot; Art Mecho is the tactical armor in the graphic battle for new worlds of desire, in which we so-called consumers are finished with desire for something, some things. We embrace the infinitesimal variation that draws anger from the core of cute, from tiny animals, dolls, and an array of involuted styles that present us with desire in an ensemble, with desire in and of worlds. So don't tell us that our delight in false images makes us traffickers of the inscrutable Orient. Art Mecho takes on the mecha powers of the false, and the retro of Art Mecho is not that of the backward glance over the shoulder, of historical timeliness and fashion, of futurism and militarism, but of those little bits of history repeating. This is the retro of Retro Direct, a mechanism that allows different gearings when peddling backward, jerkily moving over the rubble of history. [End Page ix] Frenchy Lunning Frenchy Lunning is a professor at the Minneapolis College of Art and Design and codirector of SGMS: Schoolgirls and Mobilesuits, a weekend workshop there. Thomas LaMarre Thomas LaMarre is a professor at McGill University and author of Shadows on the Screen: Tanizaki Jun'ichirô on Cinema and Oriental Aesthetics (2005) and Uncovering Heian Japan: An Archaeology of Sensation and Inscription (2000). Copyright © 2007 Regents of the University of Minnesota
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.597 | 0.382 |
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".