Composing with the Archive: Developing a Creative Sound Heritage
Bibliographic record
Abstract
A presentation and playback of a composition and accompanying visuals at a British Library event - related video in link."Unlocking Our Sound Heritage: Preserve, Inspire, Enable"An all-day conference will conclude the ambitious five-year, UK-wide project Unlocking Our Sound Heritage. The project, partly funded by the National Lottery Heritage Fund, has preserved, provided access, and raised awareness and enjoyment of thousands of the UK’s rare and unique sound recordings at the British Library and at 10 hub partners across the UK.With an exciting programme of talks, performances and discussions that will bring together voices from across the heritage sector, this conference will take you through this journey and will explore the value of sound archives and ask what else needs to be done to save sound heritage. Themes will include:• Preserve: learn about the nuts and bolts of preserving sounds and enabling access through digitisation, cataloguing and copyright• Inspire: hear the different ways we involved and engaged audiences with sound heritage and listen to creative performances from artists who transformed archival recordings• Enable: join the discussion about the future of sound heritage and hear from voices across the sectorThursday February 9th, Canada Water Theatre, London
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".