Krzesiny i Kreising – między pamiętaniem a pomijaniem. Polskie miasteczko wobec historii, pamięci i rywalizacji w cierpieniu
Bibliographic record
Abstract
The area around Krzesiny, located near the city of Poznań, Poland, witnessed several dark events during World War II: Germans oppressed the local population, culminating in a terrorizing action dubbed “akcja krzesińska;” also, a forced labor camp, named “Kreising,” was built near the township, housing mainly Jews. After the war, the suffering in Krzesiny was remembered, but selectively – “akcja” and other forms of Polish suffering were commemorated, while the camp was not. By exploring the “lieux de mémoire” in Krzesiny – dynamics of memory in a small township in Poland – this paper uses localized research to address the issue of gaps in collective memory and commemoration. We briefly look at the relevant history, Polish memory regarding wartime events in Krzesiny, and the postwar dynamics of collective memory. Discussing the latter, we identify a new phenomenon at work, one which we dub “collective disregard” – group neglect of the past of the “Other” that occurs without clear intent. We argue that “collective disregard” is an issue that naturally occurs in the dynamics of memory. By making a deliberate investment in balanced remembrance and commemoration, societies can counter the tendencies of “disregard” and curb the controversies of competitive victimization claims, also called “competitive martyrdom”.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".