Bibliographic record
Abstract
Translation, Heterogeneity, Linguistics — As an American translator of literary texts I devise and execute my projects with a distinctive set of theoretical assumptions about language and textuality, assumptions that highlight the power relations in any cultural situation and that therefore carry ethical and political implications for translation. Yet these assumptions, derived from recent European developments in literary and cultural theory (notably poststructuralism and postmarxist sociology), run counter to the linguistics-oriented approaches that currently dominate translation research and translator training, and that tend to construe language, textuality, and hence translation as relatively value-free means of communication. My article describes my conception of translation, considers how it has informed my recent translation projects — both the selection of foreign texts and the development of discursive strategies — and then examines its differences to linguistics-oriented approaches that are based on pragmatics and text linguistics. My aim is not to suggest that such approaches be abandoned, but rather that they be reconsidered from a different theoretical and practical orientation — one that will in turn be forced to rethink itself.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".