Folding and Unfolding: Balancing Openness and Transparency in Open Source Communities
Bibliographic record
Abstract
Open source communities rely on the espoused premise of complete openness and transparency of source code and development process. Yet, openness and transparency at times need to be balanced out with moments of less open and transparent work. Through our detailed study of Linux Kernel development, we build a theory that explains that transparency and openness are nuanced and changing qualities that certain developers manage as they use multiple digital technologies and create, in moments of needs, more opaque and closed digital spaces of work. We refer to these spaces as digital folds. Our paper contributes to the extant literature by providing a process theory of how transparency and openness are balanced with opacity and closure in open source communities according to the needs of the development work; by conceptualizing the nature of digital folds and their role in providing quiet spaces of work; and, by articulating how the process of digital folding and unfolding is made far more possible by select elite actors’ navigating the line between the pragmatics of coding and the accepted ideology of openness and transparency.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".