Using the Model of Regulation to Understand Software Development Collaboration Practices and Tool Support
Why this work is in the frame
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Bibliographic record
Abstract
We developed the Model of Regulation to provide a vocabulary for comparing and analyzing collaboration practices and tools in software engineering. This paper discusses the model's ability to capture how individuals self-regulate their own tasks and activities, how they regulate one another, and how they achieve a shared understanding of project goals and tasks. Using the model, we created an "action-oriented" instrument that individuals, teams, and organizations can use to reflect on how they regulate their work and on the various tools they use as part of regulation. We applied this instrument to two industrial software projects, interviewing one or two stakeholders from each project. The model allowed us to identify where certain processes and communication channels worked well, while recognizing friction points, communication breakdowns, and regulation gaps. We believe this model also shows potential for application in other domains.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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