A Comparative Analysis of the Use of GitHub by Librarians and Non-Librarians
Bibliographic record
Abstract
Abstract Objective – GitHub is a popular tool that allows software developers to collaborate and share their code on the web. Librarians have adopted GitHub to support their own work, sharing code in support of their libraries. This paper asks: How does librarians’ use of GitHub compare to that of other users? Methods – To retrieve quantitative data on GitHub users, we queried the GitHub APIs (application programming interfaces). By assembling data on librarians’ use of GitHub, as well as on a comparison group, we provided preliminary comparisons of these two samples. We analyzed and visualized this data across a number of variables to offer salient insights as to how librarians compare to randomly selected GitHub users. Results – Librarians regularly use a more diverse range of programming languages than the comparison group, hinting at a broad range of possible uses of code in libraries. While the librarians’ sample group did not demonstrate statistically significant differences from the comparison group on most measures of activity and popularity, they scored significantly higher in reach and productivity than the comparison group. This could be due to librarians’ greater longevity on GitHub, as well as their greater investment in GitHub as a tool for sharing. Conclusion – Our data suggest that librarians are actively building their libraries with code and sharing the results. While it was unclear whether librarians were more active or popular on GitHub than the comparison group, it was clear that they demonstrated statistically significant outperformance in terms of reach and productivity. To explain these findings, we hypothesized that librarians’ embrace of GitHub is in line with widely held values of “openness” in the library profession.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".