Bibliographic record
Abstract
In this essay self‐translation will not be addressed as some kind of freakish accident of nature, but rather as the proverbial tip of the iceberg. Beneath the water dwell many more bi‐ or even multilingual writers, to which we gain access by going beyond traditional models of literary history and criticism. In fact, the best‐known cases of self‐translation (Beckett, Nabokov, and Green yesterday, Huston, Semprun, and Dorfman today) embody but one of two categories: “horizontal” transfers between symmetric pairs of widespread languages. In many other instances, however, “asymmetric” linguistic configurations saddle the act of self‐translation. At least three categories of self‐translators whose linguistic repertoire is characterized by such asymmetry can be distinguished: (1) “(post)colonial” writers who alternate between their native tongue(s) and the European language of the former colonial powers; (2) recent immigrant writers who expand on work begun in their home country while staking out new ground for themselves in the language of their adoptive country; (3) writers belonging to traditional linguistic minorities because of the multilingual make‐up of the State of which they are citizens. While drawing attention to the existence of those writers, this article will also develop a typology of self‐translators, thereby looking beyond the famous case of Samuel Beckett. Beckett has often been constructed as a hapax legomenon , the quintessential exception that confirms the unwritten rule of monolingual writing, a situation that stands in the way of a better, more general comprehension of self‐translation as such. In this essay, we want to show that Beckett can help us gain many precious insights into self‐translation, but only if we allow ourselves to look beyond him, instead of staying in the shadow he casts.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".