Bibliographic record
Abstract
The Rights Management Information (RMI) of a work is simply data that provides identification of rights related to that work, either directly or indirectly. RMI in this sense is not a new concept. In the realm of distribution of creative works, it may be seen as the economic analogue to the right of attribution within moral rights jurisprudence, or even permissions on files in Unix Since the beginning of time, or at least since the beginning of the creation of artistic works, authors and owners of works have wished to be identified, and so have put their name with the title on the front cover, as well as the inside of the book. In recent centuries such identifications have typically been accompanied by information specifically related to the rights in the works, such as by the insertion of copyright notices, publishers’ information, dates, disclaimers, permissions, ISBN, acknowledgements, and so forth, that are typically inserted on the verso of the title page inside the work in printed volumes. An early example can be seen above. In the last couple of decades, given the growth in the digital market in particular, the types of RMI accompanying works have shown increased variety, and some would even say that RMI only became meaningful in the digital era. This paper addresses some of the technologies that are being used to attach RMI to works, especially works distributed in a digital format. It also looks at the potential RMI-related treaty obligations, and examines suggested and implemented legal protection for these rights in Canada.
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 | 0.015 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.551 | 0.472 |
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".