Bibliographic record
Abstract
After the fall of communism in 1989–1991, Poland and Ukraine could have become partners in international, economic, and cultural fields. Yet despite many positive achievements, the contemporary Polish-Ukrainian cooperation did not fully develop. Among many reasons that slow down the Polish-Ukrainian rapprochement, historical memories seem to be especially detrimental. The remembrances of World War II are the most destructive. Both Poles and Ukrainians understand that the only way to change this situation is to study and discuss the common history. A list of works on Polish-Ukrainian relations during World War II is long. Yet most of these publications offer broad pictures and present Polish-Ukrainian relations in general or in particular regions, such as Volhynia (Wołyń) or Eastern Galicia. This microstudy, devoted to the town of Boryslav (Borysław) in the years 1939 to 1945, tries to show how the conflicts were born, how they became embedded in human memory, and, finally, how they were transformed into historical stereotypes. The text concentrates on the crucial moments of World War II in Boryslav and describes how Poles and Ukrainians reacted differently to the consecutive challenges and how these various reactions shaped their relationship. The article ends with a conclusion that the five years of the war tore apart the Poles and Ukrainians of Boryslav and the post-1945 iron Polish-Soviet border divided the both sides and created a situation in which World War II attitudes froze for a long time.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".