Bibliographic record
Abstract
Augmented reality (AR) harbors an ocularcentric bias toward visual augmented reality (VAR), at the expense of auditory augmented reality (AAR). The latter is more readily available, technologically simpler and lower cost, with great potential for improving human well-being, what we call AAR for Well-being (AAR4W). In our chapter, we discuss the history of AR from this perspective, then present several AAR4W projects, each extending reality with acousmatic sound, as informed, in varying proportions, by disciplines of sound art, electroacoustic composition, ethnomusicology, and health sciences, including context, technical descriptions, experience, and future implications. Each is designed to enhance well-being using soundscapes, which we define as steady-state, nonperiodic sound, primarily admixtures of natural, musical, and synthetic sounds. Soundscapes offer enormous potential to alter mood and promote mental health, while remaining subliminal. Our projects are designed to support reflection, meditation, relaxation, and focus, with explicit intentions ranging among aesthetic priorities of sound artists, multicultural educational priorities of ethnomusicologists, and therapeutic priorities of health care providers. We consider soundscapes an ideal AR vehicle for well-being, as they naturally blend seamlessly with the real world, mixing harmoniously with each other, requiring minimal technological intervention or cost, and fading non-distractively into the perceptual background, unlike VAR presentation of visual information, which tends to be perceptually and cognitively disruptive.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".