Problems, Solutions, and Success Factors in the openMDM User-Led Open Source Consortium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it