Lviv and Chernivtsi: Two Memory Cultures at the Western Ukrainian Borderland
Bibliographic record
Abstract
Despite geographical proximity and comparable historical development since the fall of the Soviet Union, Lviv and Chernivtsi betray different approaches to commemorating the past. This might point to the existence of different cultures of memory that sustain a narrative about acceptance or rejection of ethnic diversity. But the cultures of memory in the cities also have common characteristic, namely, contemporary urbanites form their attitudes towards the past not through personal experience and family transmission of past memories but through prosthetic memory, which relies on hearsay, media, literature, popular culture and the arts. When deliberate choice comes to the fore in building various identity projects, the work of stitching together contradictory historical representations is guided not so much by path-dependent logic of collective memory as by present-day expediency and power games of different mnemonic actors. Therefore, this paper argues that the most observable trend in the cultures of memory in Lviv and Chernivtsi is pillarization, i.e., an agreement among external and internal memory entrepreneurs and marketeers that each population group is the custodian of its “own” heritage. Nevertheless, ultimately the condition of heritage envisioned in the two cities seems to be an assimilationist “incorporation-to-the-core” model, where the core consists of various versions of the Ukrainian national heritage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".