Replication package for paper: "What do developers talk about open source software licensing?"
Bibliographic record
Abstract
This is the dataset used in the respective research work. The abstract is available below. If you want to cite this work, please use: Georgia M. Kapitsaki, Maria Papoutsoglou, Daniel German and Lefteris Angelis, What do developers talk about open source software licensing?, to appear in the Proceedings of the Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020. Free and open source software has gained a lot of momentum in the industry and the research community. Open source licenses determine the rules, under which the open source software can be further used and distributed. Previous works have examined the usage of open source licenses in the framework of specific projects or online social coding platforms, examining developers specific licensing views for specific software. However, the questions practitioners ask about licenses and licensing as captured in Question and Answer websites also constitute an important aspect toward understanding practitioners general licenses and licensing concerns. In this paper, we investigate open source license discussions using data from the Software Engineering, Open Source and Law Stack Exchange sites that contain relevant data. We describe the process used for the data collection and analysis, and discuss the main results. Our results indicate that clarifications about specific licenses and specific license terms are required. The results can be useful for developers, educators and license authors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.112 |
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".