Bibliographic record
Abstract
This paper builds on the notion of crypto-languages, or hidden languages, to narrate the author’s coming to writing and translation. His novels are discussed as all including one aspect or another of crypto-language. For example, Russian becomes the key to salvation for Sonya, who doesn’t know how to speak it, in Sonya & Jack, and a clinical psychologist in the former Yugoslavia admits in The Speaking Cure to knowing that his patients lie to him, but that behind every lie lies the truth. The author himself learned the difference between “real” foreign languages—French, German or Spanish—and cryto-languages—Polish, Czech or Yiddish—during his childhood in Chicago. The experience of learning French forged in him the desire to write, which in turn created the desire to translate that is described here as a kind of voyeurism. The title of the paper refers to the feeling one has while reading some translated fiction: “I can do better than that!” Translation, as a form of writing, can improve the original by correcting various mistakes, in the logic of the plot, for instance. But there is a difference between writing and translating: the writer writes to find out how the story will end but the translator already knows. As a result, the best way for a writer to translate is to resist reading the book before starting the translation.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".