Bibliographic record
Abstract
The pre-digital marketplace is no longer sustainable. With the imposition of digital rights management restrictions on the distribution of media, the Internet cannot promote intellectual freedom. Peer-to-peer file sharing technology helps expose the work of artists and authors to a much wider audience than previously possible. This provides an opportunity for more sales and a greater number of successful artists and authors. Yet corporate copyright owners continue to propagate the “piracy” label to discredit the idea of open access channels. This paper argues that as information professionals, librarians are in a position to promote policy change that revolutionizes the political economy of digital goods.\nThis article is helpful for readers seeking to learn more about: activism, civil disobedience, human rights, international law, libraries, media, policy, peer-to-peer (P2P) file sharing, digital rights management (DRM), BitTorrent, Gnutella, droit d’auteur and contrefaçon, user rights, librarians, piracy, digital technology, technological neutrality, copyright, digital goods, file transfer protocol, torrent indexes, copyright infringement, intellectual property, communicative citizen, media conglomerates, technological protection measures, information technology, intellectual freedom, metadata, distribution of works, digital vendors, exceptions to copyright, information users, Pirate Party \nTopics in this article include: digital revolution, developments in copyright protection, balancing user rights and property of authors/ artists, file-sharing technology, duties of librarians, rivalry in consumption, technological innovation, MegaUpload, Recording Industry Association of America, home taping scare, fair dealing, fair use, pre-digital marketplace, copyright owners, political neutrality, Internet, media industry \nAuthorities cited in this article include: Digital Millennium Copyright Act (DMCA) Anti-Counterfeiting Trade Agreement (ACTA) Copyright Modernization Act Canadian Charter of Rights and Freedoms Sony v Universal Studios A&M Records Inc. v Napster Inc. MGM Studios Inc. v Grokster Ltd. Amstrad Consumer Electronics PLC v The British Phonographic Industry Ltd. Théberge v Galerie d’Art du Petit Champlain Voltage Pictures LLD v Jane Doe
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".