Tailings and tracings: using art and social science to explore the limits of visual methods at mining and industrial ruins
Bibliographic record
Abstract
This article examines a novel approach to visual methods that artist Ben Davis has developed based on sociologist Kevin Walby’s research into decommissioned industrial sites, which is referred to here as tracing. Disrupting the over-reliance on photographic representation in visual methods in the social sciences, the authors integrate audio recordings of interviews, as well as photos, maps, and building plans for pop-up mining communities into visual art works to provide a counter-visual analysis of the landscapes depicted in Kevin Walby’s photographs of Uranium City. After reviewing literature on environmental degradation and on visual methods, the article elaborates on Ben Davis’s practice of tracing as a technique representing the feeling of decomposition and decay generated by the harms of industrial resource extraction. The authors argue that the technique of tracing excavates layered histories of place, providing a way of creating new interpretations of social and environmental issues. They then discuss how this counter-visual analysis and approach to tracing enables a trans-disciplinary and dialogical space for engagement with academics, artists, and activists to explore issues centered on land, contamination, and justice.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.012 | 0.082 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| 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".