Digital Relationality: Relational aesthetics in contemporary interactive art
Bibliographic record
Abstract
In 1998, Bourriaud proposed relational aesthetics as an art form that took interhuman relations as its content to confront the progressive commoditization of those relations and propose alternative ways of living. Twenty years later, relational aesthetics has become even more relevant as a tool to reveal the relationality between technology and each other, as our everyday social relations have been commoditized in ways previously unimaginable. Given the enormous shifts that have occurred since its inception, relational aesthetics needs revitalization. In this paper, we aim to renew relational aesthetics as ‘digital relationality,’ recognizing important critiques about a lack of antagonism from Claire Bishop and identifying ways in which incorporating relational aesthetics with interactive art may resolve many of these criticisms. We analyse four of our own artworks as examples of how merging relational aesthetics with interactive digital art can benefit both realms. We propose that applying relational aesthetics to digital media reveals the antagonism within the structures imposed by technology ordinarily taken for granted. Drawing attention to these structures, and subverting the typical uses of these platforms, allows for reflection and discourse. This can lead both artist and viewer to imagine alternative ways of living beyond the constraints we ordinarily operate within, becoming active participants in constructing a digitally relational future.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".