Bibliographic record
Abstract
There are no rules for translation. Translation is a decision-making process, and each decision point involves uncertainty. In the following article, I would like to show how, from a skopos-theoretical perspective, a top-down procedure can at least reduce uncertainty to some degree. The top level is that of the translation brief, which determines the choice of translation type and form. This is a binary decision. A documentary translation usually “documents” the pragmatics of the source text, whereas an instrumental translation gets a pragmatics of its own, for example with regard to deixis. At the next level, the translator has to deal with cultural norms and conventions. Here, the decision becomes more complex because the brief may require the reproduction of some source-culture behaviours and the adaptation of others to target-culture conventions, both in documentary and instrumental translations. The next level is that of language. We may safely assume that most translations are expected to conform to the norms of the target-language system, but there may be cases where source-language norms have to be reproduced, for example in an interlinear translation for linguistic purposes. At the last two levels, the remaining doubts have to be resolved first in line with contextual restrictions and, ultimately, the translator’s personal preferences, if necessary.
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 | 0.062 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.026 | 0.046 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".