The decolonization of education and research in Belarus and Ukraine: theoretical challenges and practical tasks
Bibliographic record
Abstract
A conference held at the European Humanities University (Vilnius, Lithuania) in late September 2023 brought together scholars and practitioners from countries directly implicated by Russia’s full-scale invasion of Ukraine. The conference’s rationale was to re-examine the social structures and content of knowledge production and dissemination in countries that used to be categorized as “the post-Soviet region” at a time when the former metropole weaponizes the humanities for justifying the war and re-colonizing newly occupied territories. With reference to the agenda formulated by such decolonial scholars as Ngũgĩ wa Thiong’o, Madina Tlostanova, and Walter Mignolo – to “decolonize the mind” and to delink from hegemonic narratives and structures of power-knowledge imposed from the imperial centre – the participants discussed possibilities for future cooperation in a de-centred, horizontal manner, and they attempted to outline new epistemologies that derive from re-discovering themselves and communicating their emergent identities outwards. Standing as a decolonizing gesture itself, the conference created a multilingual space where participants communicated in their mother tongues to express perspectives embedded in their local experiences. The conference was co-sponsored by the Ukrainian Catholic University (Lviv) and Charles University (Prague) with the financial support of the Carnegie Corporation of New York, administered by the American Council of Learned Societies.
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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 | 0.053 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.098 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.008 |
| 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".