Bibliographic record
Abstract
Although there is a large body of scholarly literature on musical copyright, very little of this work explores in a sustained and direct way the role of copyright in regulating musical memory. This paper conceptualizes sound recording as a mnemonic technology and analyzes the manner in which copyright law attempts to manage the impact of this technology on legal concepts of musical memory and authorial subjectivity. The paper analyzes the case law on “cryptomnesia” or unconscious plagiarism in the United States and Canada wherein defendants claimed not to have access to the original work and therefore could not have copied it. These contested similarities highlight the dispersion of memory and creativity across a heterogeneous network that includes composers, musicians, and producers but also institutions and machines, and leads to the present difficulty of recentering the authorial subject in legal discourses and practices. In this way, late twentieth century legal disputes over unconscious plagiarism anticipate contemporary anxieties about the entanglement of creative and consumer subjectivities with digital techniques in recent litigation campaigns against mash-up remixing, peer-to-peer file sharing, and other popular practices of online music reproduction. Then as now, copyright acts as a site for disciplining and normalising certain modes of listening to and remembering sound recordings which in turn help smooth over tensions in the field of capitalist music (re)production.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.029 |
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".