Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it