Bibliographic record
Abstract
This paper discusses the multi-phase artistic research project Resonant Atmospheres which interrogates the logics of biometric technologies by critically reconfiguring their apparatus through participatory site-responsive performance. Drawing from feminist technoscience, critical data studies, and participatory paradigms in artistic research, the project challenges the epistemic assumptions underpinning emotion recognition systems, foregrounding the socio-political and cultural situatedness of biometric data. Rather than decoding bodily signals into emotional categories, Resonant Atmospheres transduces biological signals into ambient audiovisual environments, cultivating collective affective conditions that exceed the semantic leap common in computational deduction. In doing so, the project embraces affect as relational, contingent, and distributed—positioning biosensors not as instruments of truth-making, but as relational interfaces for co-construction of experience. Through public activations and feedback sessions with the audience, the work reorients biometric sensing away from extraction and toward relational resonance, generating atmospheres in which the affective dimensions of complex experiences such as migration can be sensed without being represented on a narrative level. The contribution offers both a critique of dominant data regimes and a proposition for how affective technologies can be subverted toward aesthetic and collective reimagining.
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 | 0.034 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.164 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| 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".