When does the “Soviet” end? Archival activism and collaborative anthropology in wartime Ukraine
Bibliographic record
Abstract
This study explores the impact of war in Ukraine on religious minority communities and their archives, shedding light on their vulnerability and newfound agency amidst Russia’s military aggression. The archives spotlighted by this research were either relocated, smuggled, destroyed, stolen, or, conversely, opened after several decades of being concealed from outsiders’ eyes. What unites them all is their shared history rooted in the Soviet past – a legacy of suppression, secrecy, and control that continues to shape these religious communities. The ongoing conflict has brought this legacy into sharper relief, exposing shadowy practices and structures, including those within religious life. Against the war’s backdrop of destruction and the “memory wars” fuelling the conflict, archival activism has assumed a new significance. Community archives emerge as contested sites of power, memory, and historical agency, serving both as custodians of silenced counter-memories and as vital tools for community activism. By rescuing their endangered historical legacy and revisiting and revising past narratives, religious communities reclaim agency over their histories and address the wounds of suppression and conflict. This process not only offers a framework for interpreting the present crisis but also serves as a means of healing and a source of resilience.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| 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".