The Aesthetics Of Causality: A Descriptive Account Into Ecological Performativity
Bibliographic record
Abstract
In this paper, I offer a perspective into a creative research practice I have come to term as Ecological Performativity. This practice has evolved from a number of non-linear audiovisual installations that are intrinsically linked to geographical and everyday phenomena. The project is situated in ecological discourse that seeks to explore conditions and methods of co-creative processes derived from an intensive data-gathering procedure and immersion within the respective environments. Through research the techniques explored include computer vision, data sonification, live convolution and improvisation as a means to engage the agency of material and thus construct non-linear audiovisual installations. To contextualize this research, I have recently reoriented my practice within recent critical, theoretical, and philosophical discourses emerging in the humanities, sciences and social sciences generally referred to as ‘the nonhuman turn’. These trends currently provide a reassessment of the assumptions that have defined our understanding of the geo-conjunctures that make up life on earth and, as such, challenge the long-standing narrative of human exceptionalism. It is out of this reorientation that the practice of Ecological Performativity has evolved.
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.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.081 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".