Translating the Chthulucene: Rasa Smite and Raitis Smits’ Atmospheric Forest as Post-Anthropocentric Visuality
Bibliographic record
Abstract
Depicting the Anthropocene is a fraught endeavor, as visual documentation often falls back on an illustrative mode that attempts to encapsulate the immensity of climate change in a single snapshot. Similarly, even the term Anthropocene is contested territory, as it is arguably too myopic to account for the full range of activities occurring in our present moment of ecological crisis. Pointing to the need to decenter humans and engage with the emergent, multilayered entanglements that compose our world, Donna Haraway proposes the “Chthulucene” as a more fitting conceptualization of our current epoch. In this paper, I argue that visual culture originating from a Chthulucenic perspective provides an opportunity to transcend the crushing anthropocentrism plaguing conventional documentation of climate change, and offers compelling models for multispecies aesthetic experiences and communications.
 My discussion focuses on Latvian artists Rasa Smite and Raitis Smits’ Atmospheric Forest (2020), a virtual reality installation that exposes the interplay between a Swiss alpine forest, the atmosphere, and global warming. Trees breathe, taking in carbon dioxide and exhaling oxygen along with large amounts of volatiles and resin, an exchange imperceptible to the naked eye. Atmospheric Forest visualizes and sonifies this process, immersing the viewer in a point-cloud data scan of the forest. I argue that the piece functions as what Sean Cubitt calls “geomedia,” or data collection and visualization methods that crucially translate non-human phenomena into humanly observable forms. By making visible intricate ecological relations between living and nonliving actors, Atmospheric Forest successfully resists the limitations of Anthropocene visuality and operates within a Chthulucenic worldview. In doing so, the work serves as an access point into some of the interlaced connections of Gaia, as theorized by James Lovelock and Lynn Margulis, and ultimately encourages viewers to pay closer attention to these complex ecologies and care for their biodiverse companions.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".