Crowdsourcing Identification of License Violations
Bibliographic record
Abstract
Free and open source software (FOSS) has created a large pool of source codes that can be easily copied to create new applications. However, a copy should preserve copyright notice and license of the original file unless the license explicitly permits such a change. Through software evolution, it is challenging to keep original licenses or choose proper licenses. As a result, there are many potential license violations. Despite the fact that violations can have high impact on protecting copyright, identification of violations is highly complex. It relies on manual inspections by experts. However, such inspection cannot be scaled up with open source software released daily worldwide. To make this process scalable, we propose the following two methods: use machine-based algorithms to narrow down the potential violations; and guide non-experts to manually inspect violations. Using the first method, we found 219 projects (76.6%) with potential violations. Using the second method, we show that the accuracy of crowds is comparable to that of experts. Our techniques might help developers identify potential violations, understand the causes, and resolve these violations.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".