Membership Herding and Network Stability in the Open Source Community: The Ising Perspective
Bibliographic record
Abstract
The aim of this paper is twofold: (1) to conceptually understand membership dynamics in the open source software (OSS) community, and (2) to explore how different network characteristics (i.e., network size and connectivity) influence the stability of an OSS network. Through the lens of Ising theory, which is widely accepted in physics, we investigate basic patterns of interaction and present fresh conceptual insight into dynamic and reciprocal relations among OSS community members. We also perform computer simulations based on empirical data collected from two actual OSS communities. Key findings include: (1) membership herding is highly present when external influences (e.g., the availability of other OSS projects) are weak, but decreases significantly when external influences increase, (2) propensity for membership herding is most likely to be seen in a large network with random connectivity, and (3) for large networks, when external influences are weak, random connectivity will result in higher network strength than scale-free connectivity (as external influences increase, however, the reverse phenomenon is observed). In addition, scale-free connectivity appears to be less volatile than random connectivity in response to an increase in the strength of external influences. We conclude with several implications that may be of significance to OSS stakeholders in particular, and to a broader range of online communities in general.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".