Think-Aloud-Based Translation Process Research: Some Methodological Considerations
Bibliographic record
Abstract
Mainly structured around issues revealed in a questionnaire survey among 25 eminent translation process researchers worldwide, this paper deals with methodological issues in think-aloud-based translation process research from two perspectives: theoretical and practical. It argues that there is no strong evidence suggesting that TAP significantly changes or influences the translation process, though TAP’s validity and completeness in a specific study might depend more or less on several variables. TAP and such recording methods as keystroke logging and eye tracking serve different specific research purposes, so they can be combined in a multimethod study to answer more complex research questions. Several research designs are available for a multimethod study, and researchers are encouraged to try designs other than one-shot case studies or convergence design. As for the research procedure, this paper touches upon how to transcribe and analyze the protocols. Many stereotypes in this field have been problematized. For example, this paper suggests that researchers transcribe as much as necessary rather than doing a “complete” transcription, or they can even skip the step of transcribing; in choosing test materials, researchers do not have to choose whole passages; they can use a group of sentences.
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 | 0.422 | 0.435 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".