Bibliographic record
Abstract
File sharing, and particularly the sharing of music files, has been labeled ‘piracy’, ‘theft’ and ‘free-riding’ by its detractors, particularly those in the music industry. The phenomenon is lauded by certain supporters, such as Kembrew McLeod, as well as by its “practitioners”, for reasons ranging from the principled (promotion of sharing or the public domain) to the practical (perceived overcharging in the industry prior to the practice, or the impossibility of stopping it). A recent study by Geert Demuijnck has effectively questioned the free-riding charge from the perspective of the traditional analytic and economic literature on free-riding. In my view, one might also call upon virtue-based notion of ethics to justify the morality of file sharing in the modern context. This discussion will necessarily turn to context and an understanding of the informal normativity that surrounds music file sharing. Given the communicative and social importance of the practice of music file sharing in the modern context, particularly among younger users, one can readily understand the virtues of sociability and friendship that are developed by this practice, as well as the development of expression and creativity on an individual level. Sharing is thus a key practice linked to virtue, and not necessarily to vice. I shall make an argument, again particularly with regard to music, that such a sharing ethos has always been part of the way that music has been written, performed and appreciated. Finally, I shall argue that current normative structures ought to be adapted to reflect this more profound understanding of the impulse to share music.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.067 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".