The Politics of a Good Picture: Race, Class, and Form in Jeff Wall's<i>Mimic</i>
Bibliographic record
Abstract
Mimic(1982) is an early and much discussed picture by the Canadian photographer Jeff Wall, with the discussion centering largely on two topics: its subject matter and its setting (fig. 1). The subject is racism, and in this regardMimicis “characteristic,” as Wall's best critic, Michael Fried, has observed, “of Wall's engagement in his art of the 1980s with social issues”(Why235). Subsequently, as Fried also notes, “Wall has tended to distance himself from the overtly political concerns that are front and center in works likeMimic”(64). Indeed, in recent interviews Wall has insisted on this distance, remarking, for example, that “[t]wenty-five years ago I thought subject matter had some significance in itself” and going on to say that“Mimicwas about racism in some way, about hostile gestures between races, but I'm glad the picture itself is good and it doesn't need that to be successful. Now I try to eliminate any additional subject matter—those things are for other people, they're not my problem” (Denes). His point here is not exactly thatMimicisn't antiracist—actually, its antiracism is so obvious and uncontroversial that a recent critic, Régis Michel, has complained that it “verges on political correctness” (63). The idea is rather that the success of the picture—the fact that it's a “good” picture—has nothing to do with those politics. Which leaves open the question of whether the picture's success has nothing to do with any politics or nothing to do with the particular politics of antiracism. In other words, is the picture's success independent of politics as such? Or is there a politics of the good picture?
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".