A geographical analysis of knowledge production in computer science
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
We analyze knowledge production in Computer Science by means of coauthorship networks. For this, we consider 30 graduate programs of different regions of the world, being 8 programs in Brazil, 16 in North America (3 in Canada and 13 in the United States), and 6 in Europe (2 in France, 1 in Switzerland and 3 in the United Kingdom). We use a dataset that consists of 176,537 authors and 352,766 publication entries distributed among 2,176 publication venues. The results obtained for different metrics of collaboration social networks indicate the process of knowledge creation has changed differently for each region. Research is increasingly done in teams across different fields of Computer Science. The size of the giant component indicates the existence of isolated collaboration groups in the European network, contrasting to the degree of connectivity found in the Brazilian and North-American counterparts. We also analyzed the temporal evolution of the social networks representing the three regions. The number of authors per paper experienced an increase in a time span of 12 years. We observe that the number of collaborations between authors grows faster than the number of authors, benefiting from the existing network structure. The temporal evolution shows differences between well-established fields, such as Databases and Computer Architecture, and emerging fields, like Bioinformatics and Geoinformatics. The patterns of collaboration analyzed in this paper contribute to an overall understanding of Computer Science research in different geographical regions that could not be achieved without the use of complex networks and a large publication database.
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 | 0.006 |
| 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