Public access to information and the creation of an ‘information commons’
Bibliographic record
Abstract
Abstract Intellectual property (IP) and intellectual property rights (IPRs) have increased in social, political, and economic importance in North America over the past two decades. There has been much talk of how we have, or are currently in the process of shifting to, an ‘information economy’ and an ‘information society’; and indeed, ‘information’ has become an increasingly valuable property, in the form of books, music, motion pictures, and corporate logos and designs. The major holders of this valuable IP have worked hard to have the laws protecting IPRs strengthened, with various consequences for public accessibility. The first part of this paper begins with a brief definition of copyright and a description of just what copyright was originally designed to protect. The second part of this paper focuses on the alternatives to copyright that are being developed in the digital realm, particularly for computer software and the Internet. As part of the battles that are currently being fought over control of this relatively new and somewhat unregulated medium of distribution, we will focus on the ‘open source’ software movement and the attempts to create ‘information commons’ that act to ensure the widest possible public accessibility to information.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".