Bibliographic record
Abstract
In this work I analyze and interpret Polish political field as a field of memory. I make three claims. First, I claim that programmatic identities of Polish political parties are weak. Despite this weakness political competition remains fierce, because parties fashion enduring political identities. I identify three mainstream political identities of political actors in Poland, given by their temporal orientation and their judgment of communism. Second, I claim that the field of the political competition predicated on the turn to the past and on moral opprobrium is the particular achievement of the party that captured political power in Poland in 2015. Similarly to its 2005 electoral success, the party narrated the country’s main problem as communist state-capture. It claimed that (former-) communists and their post-dissident allies captured political, material, and symbolic levers of power. This way of presenting the problem polarized the field, casting political opponents as essential enemies, and casting the narrators as country’s saviors. Third, this achievement was possible because the party narrated communism as essentially and existentially anti-Polish: it presented it as equal to Nazism, it made it foreign, and it made it coincidental with Jewishness. It then launched such discursive “weapon” against its present-day opponents.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".