Decolonizing Sociological Knowledge: A Bibliometric Exploration of Postcolonial and Global South Contributions (2014–2024)
Bibliographic record
Abstract
The decolonization of sociological knowledge is critically important for challenging enduring Eurocentric dominance and epistemic inequalities in the discipline, yet empirical assessments of Global South contributions remain scarce. This study addresses this gap by conducting a bibliometric analysis (2014–2024) of 496 Scopus-indexed documents to examine the visibility and integration of postcolonial and Global South scholarship in sociology. Using VOSviewer for co-citation and bibliographic coupling analyses, the study reveals persistent marginalization: fewer than 15% of citations in high-impact journals reference works from Asia, Africa, or Latin America, while 62% of decolonization-related publications originate from the U.S. and Canada. Key findings highlight the hegemony of Western theoretical canons (e.g., Bourdieu, Foucault) and the exclusion of Southern epistemologies (e.g., Ubuntu, Buen Vivir), exacerbated by metric systems privileging Scopus/WoS-indexed journals. The results underscore the need for structural interventions—curricular reforms, inclusive citation practices, and alternative databases—to democratize knowledge production. This research contributes empirical evidence to decolonial debates, offering pathways to transform sociology into a pluralistic discipline that centers marginalized voices and addresses global epistemic injustices.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.089 | 0.133 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".