Bibliographic record
Abstract
The first Margaret Atwood book appeared in 1984 in Hungarian translation but that does not mean that Európa Publishing House did not follow Atwood’s literary work closely during Communism. Both her prose and poetry were reviewed, often shortly after the original English language publication. The paper examines twenty-two reviewing in-house documents that Európa Publisher used as part of the selection process and an informal censorship procedure. First, the study draws the cultural context for the in-house selection tools and then identifies key themes in the anonymized reviewing documents of the era, such as: possible titles for the books, poetry weighed on scales, the practice of multiple reviewing, social classes in translation, relying on paratexts, the reputation of international success behind the Iron Curtain, and in what way is this literature “Canadian”? The paper tracks the publishing paths of all Atwood books reviewed during and immediately after the political change of 1989, concluding that the tools for selecting books for translation have changed, not only due to the political change, but as a result of the accelerated publishing practices that focus on bestseller lists, literary prizes, pitches of literary agencies and a network of personal contacts.
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 | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".