Against academic “resourcification”: collaboration as delinking from extractivist “area studies” paradigms
Bibliographic record
Abstract
This article engages Asia Bazdyrieva’s idea of the “resourcification” of Ukraine – that is, the reduction of Ukraine in Soviet and Western geopolitical imaginations to a mere extraction resource – to develop and criticize the idea of “academic resourcification.” The author argues that Western researchers have often treated Ukrainian (and other non-Western) subjects as extraction resources, mining their expertise and knowledge, without acknowledging their agency or contributions in their work. The article argues for the decolonization of Western academic practice in the form of “delinking” from such exploitative and extractivist paradigms of knowledge production and instead aspiring, in the words of the decolonial scholar Walter Mignolo, to “thinking and doing otherwise.” Asking what it means to decolonize academia, the article turns for inspiration to Ukrainian decolonial researcher-artist-activists, considering the ways in which these individuals are modelling more equitable and ethical forms of knowledge production. The article ends by advocating collaborative methods – that is, the co-production of knowledge with local thinkers, rather than about them – as a productive model for Western scholars in their efforts to decolonize their research.
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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 | 0.044 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.137 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".