Diversity in Software Engineering Conferences and Journals
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
Diversity with respect to ethnicity and gender has been studied in open-source and industrial settings for software development. Publication avenues such as academic conferences and journals contribute to the growing technology industry. However, there have been very few diversity-related studies conducted in the context of academia. In this paper, we study the ethnic, gender, and geographical diversity of the authors published in Software Engineering conferences and journals. We provide a systematic quantitative analysis of the diversity of publications and organizing and program committees of three top conferences and two top journals in Software Engineering, which indicates the existence of bias and entry barriers towards authors and committee members belonging to certain ethnicities, gender, and/or geographical locations in Software Engineering conferences and journal publications. For our study, we analyse publication (accepted authors) and committee data (Program and Organizing committee/ Journal Editorial Board) from the conferences ICSE, FSE, and ASE and the journals IEEE TSE and ACM TOSEM from 2010 to 2022. The analysis of the data shows that across participants and committee members, there are some communities that are consistently significantly lower in representation, for example, publications from countries in Africa, South America, and Oceania. However, a correlation study between the diversity of the committees and the participants did not yield any conclusive evidence. Furthermore, there is no conclusive evidence that papers with White authors or male authors were more likely to be cited. Finally, we see an improvement in the ethnic diversity of the authors over the years 2010-2022 but not in gender or geographical diversity.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| 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