Analyzing Citation Frequencies of Leading Software Engineering Scholars
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
It is understandable that sponsors of research activities are interested in assessing the work of scholars they (plan to) support although this is not a simple undertaking. Until today, there is no obvious approach for objectively measuring and comparing the quality of research results from different disciplines. Hence, counting publications has been used for long time as a substitute to deal with this challenge and only recent technological advances have fostered the usage of so-called citation indices (such as the h-index) for this purpose. Although this approach is as disputed as all previous ideas in this context, we feel it is about time to investigate the expressiveness of modern citation analysis approaches in computer science more closely. In order to do that, we have chosen the area of software engineering and created a first comprehensive ranking, illustrating citation values of world class scholars by analyzing the work of almost 700 researchers in this field. We have found that top h-index scores in software engineering are around 60 while top-notch g-indices start at around 130 when Google Scholar and Publish or Perish, the quasi standard tools for this purpose are used. Clearly, the results of our study are influenced by the coverage of these tools so that we have also analyzed Google Scholar and found it having a very high coverage of software engineering publications. Hence we are convinced to have collected good quality results that will allow our community to better judge and use citation numbers in the future.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.015 |
| 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