The Governance and Control of Open Source Software Projects
Bibliographic record
Abstract
A comprehensive set of governance mechanisms and dimensions were investigated to identify combinations of mechanisms that are effectively used together in on-going volunteer-based open source software (OSS) projects. Three configurations were identified: Defined Community, Open Community, and Authoritarian Community. Notably, Defined Community governance had the strongest coordination and project climate and had the most extensive use of outcome, behavior, and clan control mechanisms (controller driven). The controls in the Defined Community governance configuration appear to effectively enable open, coordinated contribution and participation from a wide variety of talented developers (one of the virtues of open source development) while managing the development process and outcomes. The results add to our theoretical understanding of control in different types of information systems projects, as the combination of control modes found in OSS projects is different from those found in previous research for internal or outsourced information systems development projects. This could be due to unique features of OSS projects, such as volunteer participation and the controller being part of the development team. The results provide guidance for practitioners about how to combine 19 identified governance mechanisms into effective project governance that stimulates productive participation.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".