Problems, Solutions, and Success Factors in the openMDM User-Led Open Source Consortium
Bibliographic record
Abstract
Open-source software (OSS) development offers organizations an alternative to purchasing proprietary software or commissioning custom software. In one form of OSS development, organizations develop the software they need in collaboration with other organizations. If the software is used by the organizations to operate their business, such collaborations can lead to what we call “user-led open-source consortia” or “user-led OSS consortia”. Although this concept is not new, there have been few studies of user-led OSS consortia. The studies that examined user-led OSS consortia did so through the lens of OSS, but not from the inter-company collaboration perspective. User-led OSS consortia are a distinct phenomenon that share elements of inter-company collaboration, outsourcing software development, and vendor-led OSS development and cannot be understood by using only a single lens. To close this gap, we present problems and solutions in inter-company collaboration, outsourcing, and OSS literature, and present the results of a single-case study. We focus on problems in the early phases of a user-led open-source consortium, the openMDM consortium, and the solutions applied to these problems. Furthermore, we present the factors which lead this consortium to sustained growth.
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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 | 0.043 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".