Understanding and Auditing the Licensing of Open Source Software Distributions
Bibliographic record
Abstract
Free and open source software (FOSS) is often distributed in binary packages, sometimes part of GNU/Linux operating system distributions, or part of products distributed/sold to users. FOSS creates great opportunities for users, developers and integrators, however it is important for them to understand the licensing requirements of any package they use. Determining the license of a package and assessing whether it depends on other software with incompatible licenses is not trivial. Although this task has been done in a labor intensive manner by software distributions, automatic tools to perform this analysis are highly desired. This paper proposes a method to understand licensing compatibility issues in software packages, and reports an empirical study aimed at auditing licensing issues in binary packages of the Fedora-12 GNU/Linux distribution. The objective of this study is (i) to understand how the license declared in packages is consistent with those of source code files, and (ii) to audit the licensing information of Fedora-12, highlighting cases of incompatibilities between dependent packages. The obtained results - supported by feedback received from Fedora contributors - show that there exist many nuances in determining the license of a binary package from its source code, as well as cases of license incompatibility issues due to package dependencies.
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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 | 0.018 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".