Pseudo-translation as a Subset of the Literary System: a Case Study
Bibliographic record
Abstract
Abstract Persian literature is replete with pseudo-translations to the extent that if one tried to compile a complete bibliography, it would turn into an unwieldy book. Most Persian pseudo-translations belong to Iranian political literature (Okhovat, 2006). As could be guessed, identifying pseudo-translations is not a simple task as their authors want readers to believe in the ‘translationness’ of these works for various reasons. One of the most famous Iranian pieces of pseudo-translations, whose original writer has recently claimed its authorship, is the famous (in Iran) Letter of Charlie Chaplin to his daughter Geraldine. The present article examines diverse aspects of this text, including its political, historical, cultural, and literary milieu of production, and provides a critical discourse analysis of this text and highlights the original author’s ideological stance as it is embedded in this purported foreign letter. The article concludes by surmising the reasons why this famous work was published as a translation, and how it is, that the original writer has claimed authorship after more than 30 years.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".