Location-Based Analysis of Developers and Technologies on GitHub
Bibliographic record
Abstract
GitHub is a popular platform for collaboration on open source projects. It also provides a rich API to query various aspects of the public activity. This combination of a popular social coding website with a rich API presents an opportunity for researchers to gather empirical data about software development practices. There are an overwhelmingly large number of competing platforms to choose from in software development. Knowing which are gaining widespread adoption is valuable both for individual developers trying to increase their employability, as well as software engineers deciding which technology to use in their next big project. In terms of a developer's employability and an employer's ability to find available developers in their economic region, it is important to identify the most common technologies by geographic location. In this paper, analyses are done on GitHub data taking into account the developers' location and their technology usage. A web-based tool has been developed to interact with and visualize this data. In its current state of development, the tool summarizes the amount of code developers have in their public repositories broken down by programming language, and summarizes data about programmers using specific programming languages. This allows website visitors to get an immediate picture of the programming language usage in their region of interest. Future research could expand this work to technologies beyond programming languages such as frameworks and libraries.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".