A study on copyright issues of different controlled digital lending (CDL) modes
Bibliographic record
Abstract
In the recent years, CDL has been heatedly talked about, CDL should be treated objectively and rationally. Getting knowledge of CDL modes and their copyright issues is critical for sustainable development of CDL. Rather than CDL becomes a transient phenomenon as a result of many copyright hurdles. The paper will explore CDL modes by combing CDL practices and programs from research papers and official website documents of different library organizations. Then, based on legal frameworks of CDL in the US, Canada and the UK which are summarized, copyright issues of CDL modes are analyzed from perspectives of implementing institution, service resources, and usage mode. Finally, some copyright recommendations for sustainable development of CDL are proposed. We believe that library institutions can use CDL to advance their crucial mission for the public’s interest through making sense of different CDL modes and their copyright issues and implementing some proposals about copyright processing.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".