How the R community creates and curates knowledge
Bibliographic record
Abstract
One of the many effects of social media in software development is the flourishing of very large communities of practice where members share a common interest, such as programming languages, frameworks, and tools. These communities of practice use many different communication channels but little is known about how these communities create, share, and curate knowledge using such channels. In this paper, we report a qualitative study of how one community of practice---the R software development community---creates and curates knowledge associated with questions and answers (Q&A) in two of its main communication channels: the R-tag in Stack Overflow and the R-users mailing list. The results reveal that knowledge is created and curated in two main forms: participatory, where multiple members explicitly collaborate to build knowledge, and crowdsourced, where individuals work independently of each other. The contribution of this paper is a characterization of knowledge types that are exchanged by these communities of practice, including a description of the reasons why members choose one channel over the other. Finally, this paper enumerates a set of recommendations to assist practitioners in the use of multiple channels for Q&A.
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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 | 0.067 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".