Location-Based Analysis of Developers and Technologies on GitHub
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it