Invisibilizing Eurasia: How North–South Dichotomization Marginalizes Post-Soviet Scholars in International Research Collaborations
Bibliographic record
Abstract
Despite the growing complexity and multidimensionality of the system of international research collaborations, the colonial discourse casting the collaborative relationships in terms of polarized North–South antitheses persists and continues to exert influence on the nature of power distribution in the relationships, as well as on the conceptualization of some players in the relationship as superior donors, whereas others as inferior recipients. In this article, I demonstrate how dichotomic representation of the geopolitical entities involved in international research collaboration fails to acknowledge the existence and marginalizes a large and extremely talented academic community of Eurasian scholars, which has the potential to enrich and transform the global knowledge system if it becomes more actively and authoritatively involved in the international system of ideas exchange and knowledge generation. I argue that this invisibilization of post-Soviet research community is part of the logic of postcolonial governmentality, which creates the demand and draws the post-Soviet world into international trade in knowledge-related expert services, whereby profits and benefits from the trade are reaped by the countries, which self-categorize as the North, while undermining the potential of the former Soviet scholarly community to contribute to global knowledge production on equal terms.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".