Open Source Software: Free Isn't Exactly Cheap!
Bibliographic record
Abstract
Open Source Software (OSS) refers to software the is freely transferable to other users without charge, but often there are conditions placed on the user of the software, either in how the software can be used or what must be done if the software is modified or incorporated into software which was developed by the user. Each OSS license agreement carries its own restrictions. The use of OSS carries the potential of serious inherent risks of copyright infringement, which typically is not mitigated by supplier indemnities. At present, OSS is neither approved nor disapproved by the U.S. Government. This unresolved status makes program, project, and developer decisions regarding OSS difficult. Users must ensure that any use of OSS does not place it at risk of infringement and inclusion in its products does not compromise either the copyright integrity of user's ownership nor require users to disclose valuable trade secrets in return for using the OSS software. Assuming a project manager can justify these expenses and acceptable legal risk, if one is using Open Source for Mission / Safety Critical or Information Assurance, there are additional product certification processes that can add enormous cost to the code base. All these risks have to be assessed and appreciated before one can say it's free, but is it cheap!
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.059 |
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".