Proceedings of the 3rd International Workshop on Emerging Trends in Free/Libre/Open Source Software Research and Development
Bibliographic record
Abstract
A large body of research into FLOSS (Free/Libre/Open Source Software) has focused on the exemplars within the available corpus of FLOSS projects: such as Apache HTTP Server, Eclipse, and Linux. However, many other FLOSS projects exist which provide a very rich body to study and understand. By focusing on more projects that perhaps do not gain the immediate attention of researchers, we hope to broaden our knowledge of the rich ecosystems within FLOSS. Specifically, the goal of the FLOSS-3 workshop (8th in a series at ICSE) is to bring together academic researchers, industry members, and FLOSS developers for the purpose of discussing topics including analyzing competing projects within FLOSS that share the same domain, performing data collection and analysis among many FLOSS projects, examining governance models within FLOSS projects, identifying licensing paradigms of FLOSS projects, discussing the interplay of corporate involvement within FLOSS projects, social and technical interactions between FLOSS projects, and dependency analysis and reuse between FLOSS projects. We believe that this workshop will also serve as a common bridge between the ACM/IEEE (ICSE) and (IFIP) OSS research communities, thereby providing a window for others in the Software Engineering community to interact with and learn more about the advances of research into FLOSS development and communities.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".