Where is the “Global South” located in scientific research?
Bibliographic record
Abstract
Although the term “Global South” has been increasingly invoked by heads of State as a call for enhanced multilateralism and institutional reform, its academic conceptualization remains underdeveloped. Therefore, we investigate how and where scientific knowledge about the Global South is produced, using a meta-analysis of around 17,000 articles [1994–2024] indexed in Scopus database. The paper shows that authors and funding are predominantly from Global North institutions, particularly the United States. However, rising powers are increasingly active contributors, notably South Africa, India, China and Brazil. The most frequent research topics include globalization, COVID-19, climate change, gender issues, neoliberalism, decolonization and sustainability. The results also reveal the centrality of Africa in the debate, and the underrepresentation of Global South institutions in leading journals. The conclusion calls for more scientific collaboration to improve visibility of knowledge produced in the Global South institutions and suggests that ESG may be a key player.
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.
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 | 0.077 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.035 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".