Russia’s war against Ukraine: historical narratives, geopolitics, and peace
Bibliographic record
Abstract
The Russian–Ukrainian relationship has been framed by contrasting and competing historical narratives, which might be characterized as a narrative of empire and colonial rule vs. one of sovereignty and self-determination. Playing a seminal role in identity formation, the Russian imperial narrative looks to the past and the importance of state power in exercising control. Ukraine’s story, aligning itself with European and global developments, projects towards a future based on autonomy and freedom. Although intertwined, the two narratives are incompatible, providing a context for the current conflict. The identity dimension of the conflict renders it intractable, and peace appears elusive. However, by looking to the past, Russia also rejects the trajectory in global history towards – and the logic of a world system committed to – the sanctity of the sovereignty principle. Russia’s war is not simply with Ukraine but with a global community that has disavowed brute force in favour of a rules-based order. Thus, the road to peace is for Russia to reimagine its historical past and discard its mythologized identity so that it might live in concord with its neighbours and even itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".