Bibliographic record
Abstract
This paper examines the ambivalence of “destination”—namely the ambivalence of the user’s interpellation—as one of the key features of augmented reality (AR) art. It calls attention to the special status of the spectator whose participation is at once a requirement and an uncertainty, a prediction and an anxiety, a principle of localization and a questioning of the very capacity to localize. This ambivalence is endemic to AR environments which rely on mobile, networking, tracking, sensing and detection technologies. My main claim is that, as a perceptual paradigm, AR’s potential innovativeness lies in its ability to generate new ways of perceiving for the spectator or to disclose what was previously unperceived—unseen, unheard, unfelt. These ways of perceiving are structurally rooted in the ambivalence of destination. This structuring feature, however, is recurrently sidestepped by the interactive setting of AR art. Required to interact; destined to act specifically and to insert him or herself in a standardizing logic of community formation; allegedly “in direct contact” with the immediate environment despite extreme mediation: the spectator turned user, YOUuser or interactor is solicited as a destinataire (a recipient) in ways that most often counter the possibilities of AR as an ambivalent mode of destination. The paper investigates three AR environments by artists Rafael Lozano-Hemmer, Mathieu Briand, and Christa Sommerer & Laurent Mignonneau to show how these traits either counter or favour the perceptual potential of AR.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".