Open Content Licenses Without Representation: Can You Give Away More Rights Than You Have?
Bibliographic record
Abstract
Authors who are voluntarily placing their creations into the commons allow the public to build upon their work, sometimes provided that certain conditions are respected. Open content, open source or free licenses intend to facilitate sharing and reuse by lowering transaction costs. In theory, no additional negotiation or copyright or contractual related task is needed to reuse such works because authorization has been provided in advance. However, in practice, it might be uncertain whether all necessary rights have been granted or not. We consider one example of difference between the various copyleft licensing schemes which are available to those who want to place their works or data in a voluntary commons: is the licensor offering the content with a representation that it does not content elements which may infringe upon the rights of third parties, including copyright infringement, privacy, trademark or right to image which might pertain to elements of the licensed work? The article will present the different options and assess the legal consequences of offering representations, or not, and discuss the legal problems raised by waivers of warranties according to EC and national consumer and contract law of European civil law jurisdictions on the one hand, and the perspective of securing sustainable and safely reusable commons on the other hand.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.018 | 0.037 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".