On the Concept of Recognition in Media Art: Emotional reactions, empathetic interactions
Bibliographic record
Abstract
Biometric technologies have transformed recognition into an empirical and automated activity. But recognition is not just a matter of identification or surveillance. As computer systems become capable of detecting human emotion, we are reminded of philosophical approaches to recognition that place it as central activity of human self-realization and social existence. Bringing together these dual notions of recognition, this paper considers how artists are taking hold of the technical possibilities of recognition to make political the media artwork. Specifically, it turns to Karen Palmer’s interactive film RIOT (protoype) (2016), in which the narrative depends on the recognition of the participant’s emotive facial expressions, and Erin Gee’s Project H.E.A.R.T. (2017), a virtual reality artwork in which the participant’s “enthusiasm” is harnessed via a biosensor. Through these examples, the paper proposes a way to think the politics of media art by pivoting on the technologies, practices and philosophies of recognition.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".