Harmonizing Open Licenses among Online Databases of Enthusiast Communities: Challenges for the Legal Integration of Databases in the Japanese Visual Media Graph Project
Bibliographic record
Abstract
The Japanese Visual Media Graph project has created a knowledge graph for researchers working on popular Japanese visual media by combining data compiled by various enthusiast online communities. In order to open up the knowledge graph to researchers around the globe, the databases needed to be integrated legally. This article discusses the hurdles we encountered in this integration process and the solution we settled on to overcome these problems. We provide a brief look at the complexity of the legal protection afforded to databases, which we found to be an important source of problems even for communities that attempted to apply appropriate open licenses to their data. Finally, we detail how using the CC BY-NC-SA 4.0 license as a smallest common denominator and asking communities to provide us with a separate tailor-made licensing agreement helped address both the concerns of the communities and the long-term needs of the project.
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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 | 0.173 | 0.223 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.029 | 0.050 |
| Open science | 0.007 | 0.043 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".