Bibliographic record
Abstract
Abstract Sound acts as an extension of the body, created by movement and received as vibration. I am focused on the removal of a visual representation of the body as a template; to instead facilitate an embodied experience. As an embodied practitioner, I create immersive sound and media installations derived from recordings of my own moving body. The movement of sound depicts the presence of a body in motion through sensory illusion. Through embodied sonic design, my sound recordings decontextualize, abstract, and reframe the auditory experience. I physically manipulate the recording of sound to perceptually rematerialize the moving physical form during playback with two techniques: sound shadows and embodied binaural spatialization. These techniques encourage the listener to perceive sound and space with the same awareness that situates their body, such as sensation and proprioception. The perceived physical interaction within the reception of this sound is akin to a kinesthetic projection and is an engagement in spatial thinking, activating mirror neurons and kinesthetic empathy. Creating awareness through physical attunement can regulate systems out of balance by offering the embodiment of alternative states: shifting how one thinks and feels in a particular setting. My research seeks to recognize the listener’s unique perspective through their individual body.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".