Bibliographic record
Abstract
The present issue of SVMMA contains three different studies on historical sound landscape; that is, three studies on the relationship between sounds, communication, musical expressions and the environment where they were produced in the past.Research on sound landscape is relatively new, even more in the case of sound landscapes that cannot be recorded, that is, historical sound landscapes.The term sound landscape was coined in the 60s in the context of the World Soundscape Project, a catalogue of sounds of the world promoted by the Simon Fraser University (Vancouver) and led by R. Murray Schaffer, 1 in a moment when the history of senses was starting to be perceived as a legitimate field of study by different disciplines (anthropology, sociology, and communication studies among others) and as a source of knowledge of the past.Sound and landscape gradually made their way into ethnographic, literary, linguistic, and historical research, going beyond the musicological approach to treatises and scores, and the strictly iconographic approaches to musical instruments.Already in the 90s, a new field of study started gaining momentum in parallel to the studies on musical palaeography, iconographical analyses, and the reconstruction of musical instruments: archaeomusicology.This burgeoning discipline involved the archaeological study not only of sound-producing objects-that is, musical or communication instruments-but also of the spatial context related to sound production. 2The first studies on reconstruction of musical instruments were carried out by both musicians specializing in ancient music and luthiers, but over the years many other disciplines have joined the effort to shed light on this matter, such as history, archaeology, and linguistics.In a first stage, the technical and organological approaches started from analyses of the sound in relatively recent periods, particularly the Baroque and Modern ages, and then extended these studies to earlier
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.317 | 0.116 |
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".