Empirical Studies in Translation: Methodological and Epistemological Questions1
Bibliographic record
Abstract
When a qualitative leap forward is taken in any scientific discipline, the change is usually accompanied by an increased interest in research. This occurred in Translation Studies in the 1950s and in the 1990s. This paper outlines some of the most important epistemological and methodological questions faced by researchers who want to apply the so called “scientific method” to empirical research in translation. We will discuss the main steps in the research process: designing an experiment, selecting subjects or the object of study, defining experimental and control groups, controlling independent variables, choosing instruments that will measure what we want to measure and which will give us reliable data to analyse. The whole procedure should be intelligible and transparent, the objectives relevant and the results clear.
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 | 0.339 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.009 | 0.098 |
| Scholarly communication | 0.027 | 0.042 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".