The globalization of social sciences? Evidence from a quantitative analysis of 30 years of production, collaboration and citations in the social sciences (1980–2009)
Bibliographic record
Abstract
This article addresses the issue of internationalization of social sciences by studying the evolution of production (of academic articles), collaboration and citations patterns among main world regions over the period 1980–2009 using the SSCI. The results confirm the centre–periphery model and indicate that the centrality of the two major regions that are North America and Europe is largely unchallenged, Europe having become more important and despite the growing development of Asian social sciences. The authors’ quantitative approach shows that the growing production in the social sciences but also the rise of international collaborations between regions have not led to a more homogeneous circulation of the knowledge produced by different regions, or to a substantial increase in the visibility of the contributions produced by peripheral regions. Social scientists from peripheral regions, while producing more papers in the core journals compiled by the SSCI, have a stronger tendency to cite journals from the two central regions, thus losing at least partially their more locally embedded references, and to collaborate more with western social scientists. In other words, the dynamic of internationalization of social science research may also lead to a phagocytosis of the periphery into the two major centers, which brings with it the danger of losing interest in the local objects specific to those peripheral regions.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".