Bibliographic record
Abstract
Sonic narratives on fixed media can take many forms. We may find complexly nuanced sound productions that rely on a broad range of implied and/or culturally shared non-verbal cues to convey a narrative progression. But we also frequently find creative productions centred upon the human voice, much like traditional storytelling but presented in the wider variety of performed, captured, or constructed contexts enabled by technology. In those productions, human voice without a visible physical source will represent, if only in the historic sense, the essence of the acousmatic – an unseen speaker addressing assembled listeners. And, although precise listener responses to that unseen voice will certainly vary, we typically respond quite strongly when directly addressed by another human voice. What are some of the attributes of voice that can trigger those strong responses? And, more pragmatically, what questions should composers consider as we attempt to harness that power for our own creative ends? In this article, we raise some of those questions for consideration, with the hope that readers – particularly those who are also sonic creators – will seek to answer them through their own creative practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".