Bibliographic record
Abstract
Music is among the most captivating topics in cyberspace. On almost any given day, headlines highlight something new happening in the online music industry. Often the story involves litigation against anybody threatening traditional business models. Lawyers obviously aren't the only people interested in these developments, but legal issues do permeate all aspects of distributing music via the internet. So it isn't surprising that lawyers are tackling this topic more frequently. Leading information technology law practitioner Andrew Sparrow is among the latest to do so. In his new book, Sparrow attempts to offer insights into the legal aspects of conducting music-related business online. His focus is on British and European law, but there are occasional references to other jurisdictions, including the United States. He writes for those involved with various facets of the music industry, including composers, publishers, performers, managers, executives and, of course, lawyers. Although Sparrow does touch on timely topics like podcasting, ringtones and the Creative Commons, the book contains little in the way of cultural commentary. Unlike some more general works on trends in digital music, this is a technical and practical account of the range of legal challenges associated with internet-based music businesses.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.019 |
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".