Reconceptualizing the Organ through Networked Spaces: Interviews with Participants of the 2023 Global Hyperorgan Concert
Bibliographic record
Abstract
This paper examines the Global Hyperorgan concert held at the 16th annual Orgelpark Symposium in Amsterdam, Netherlands, in June 2023. The Symposium aims to reimagine the historically significant yet underused pipe organs. As part of meeting this aim, the concert featured performances linking Amsterdam and Vancouver using the Global Hyperorgan, a system that connects pipe organs via network technology. Interviews were conducted with the participating musicians to explore their experiences with this musical platform. Three key themes of primary interest to these musicians are revealed: alternative control interfaces, space and acoustics, and latency as a creative tool that drives innovations in performance, such as the use of data streams to control organs without traditional organ-playing skills. The interviews reveal that applications of network technology can significantly enrich organ performance while fostering new collaborations and interest in developing music for pipe organs.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".