Bibliographic record
Abstract
Purpose The purpose of this paper is to identify, operationalise, and test a knowledge management model in the context of electroacoustic and mixed music preservation. This operationalisation intends to provide an interdisciplinary framework for the specification of meaningful usability for idiosyncratic technological artefacts build up during the creative process of these works. Design/methodology/approach The design of the questionnaire was based on semi‐structured interviews with seven composers. The resulting questionnaire was used for an online survey targeting composers registered at electroacoustic and mixed music online associations. Data were collected from 33 composers. Findings This article demonstrates the relevance of Boisot's knowledge management model in order to categorize the knowledge involved during the creative process of electroacoustic and mixed music with spatialisation. Research limitations/implications In terms of Boisot's model operationalisation, the authors identified limitations with regards to composers' ability to discriminate between different levels of abstraction and diffusion. Since multiple agents, both human and non‐human, are involved in the creative process of electroacoustic and mixed music, further studies should address their interaction throughout the creative process. Originality/value Based on the findings of the survey, the authors propose the concept of significant knowledge as an extension of significant properties in order to provide a meaningful usability of digital objects. Since similar technologies are used in theatre, dance, and fine arts, the authors expect this research to benefit the artistic community at large in terms of preservation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".