Tailings and tracings: using art and social science to explore the limits of visual methods at mining and industrial ruins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it