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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".