Bibliographic record
Abstract
This paper* attempts to chart a prospective course for literary translation, and to a degree the criteria for literary translations in the past. In this paper, I am concerned with 'valid', and not with 'deficient' texts. A distinction between literary and non-literary language has to be made. The substance of both languages is the same, but literary language is concerned with fiction, the imagined world; non-literary language with reality; literary language is personal; non-literary language is more standardised, more conventional, and has a large number of discourses related to class and occupation; fundamentally, literary language is centred in individual people, allegorical or typical though they are, and directly in their imagined worlds; non-literary language is centred in facts, in society or in groups, in processes and in objects, often at a reporting or an indirect stage. Literary language is pre-dominantly connotative, non-literary language is denotative. Unlike non-literary language, serious literary language, which may be innovative in punctuation, words and syntax, but may also be natural and non-innovative, should not be normalised, lexically, grammatically or in the punctuation by the translator.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".