Explaining the organization of open source communities with the CPR framework
Bibliographic record
Abstract
This paper describes work-in-progress. It describes the background, research framework and some preliminary results from a PhD research on the organization of open source communities. Most open source communities are very small. However, some communities have become very popular and they connect thousands of predominantly highly skilled programmers from various parts of the world. Together these programmers create and maintain highly complex software. Wellknown examples of such communities are Apache and Linux. The software developed in open source communities has one very important characteristic: the source code of the software is open and freely available. 1 To many it is highly surprising that programmers in open source communities are able to create successful software. Two questions prevail, they are: a) how are open source communities able to deal with internal pressures like free-riding and cascading conflicts and b) how are they able to resist external pressures, created by parties who appropriate software through copyrights and patents? This paper addresses the question how programmers in open source communities organize and sustain themselves amidst these pressures. Ostrom’s (1990) eight design principles are adopted to answer this question. The two most dominant conclusions from this research are: (a) individuals in open source communities are driven by individual choice and (b) formal mechanisms have a limited role in solving the issues addressed by the design principles. This article will analyze one design principle in more detail, namely the presence of conflict resolution mechanisms. 1 The source code of software is the human-readable part of the software, which allows programmers to understand how the software works and allows them to modify the software if they choose to do so. 1
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.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".