THE NEW TOPOGRAPHICS, DARK ECOLOGY, AND THE ENERGY INFRASTRUCTURE OF NATIONS: CONSIDERING AGENCY IN THE PHOTOGRAPHS OF EDWARD BURTYNSKY AND MITCH EPSTEIN FROM A POST-ANARCHIST PERSPECTIVE
Bibliographic record
Abstract
Edward Burtynsky’s aesthetic and the New Topographic aesthetic from which it derives, I argue, should not be seen as apolitical but rather as traces of an empire in ruins and a sociality to come; that is, by employing a post-anarchist analysis, I demonstrate how Burtynsky’s photographs in his recent collection Oil, and Mitch Epstein’s images from American Power, produce an aesthetic of what Yves Abrioux calls “intensive landscaping,” or “landscaping as style, as the promise of a social spacing yet to come” (264). What Burtynsky and Epstein accomplish in their photographs related to energy in particular is “to invent relations, rather than assert ideological or cultural control” (ibid.); the place of energy extraction and transport becomes not a self-contained striation of ecological degradation, but a “place of passage,” to use Deleuze and Guattari’s terminology, a depiction of wildness and civilization in contact, assembled and reformulating the landscape into something other. The aesthetic under consideration has much in common with Timothy Morton’s “dark ecology” and Stephanie LeManager’s “feeling ecological,” theories that attempt to understand the affective connections between the infrastructure of oil capitalism and ecology (“Petro-Melancholia” 27).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| 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".