New Forms of Governance for Digital Orphans: Copyright Litigation, Licenses and Legal Information
Bibliographic record
Abstract
This chapter confronts how might we deal with some of the practical problems associated with large-scale digitisation projects, specifically with copyright issues, and even more specifically with the so-called orphan works. This chapter also contributes to the literature focused on the evolution of the global governance of Intellectual Property, and to this book about trade governance in the digital age, through a sort of case study that examines three different aspects of the governance of orphan-works issues that arise from large-scale digitisation projects such as Google Book Search: (1) class action litigation; (2) quasi-judicial licensing; and (3) rights management information practices. But first, it might help to have a few more details about Google Book Search and the issue of orphan works.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".