Bibliographic record
Abstract
In recent years, scholars have shifted from the study of translation products to the study of translation process, that is, to explore the psychological activities of translators in the process of translation in order to uncover how the human brain operates in the process of translation. In the research process, thinking-aloud protocols (TAPs) is widely used. The research on the translation process of TAPs is a part of the translation process of cognitive psychology. In the cognitive research on the translation process of TAPs, some translation researchers at home and abroad have conducted experiments. The research of TAPs translation process has its specific research object, research methods and data collection requirements. Data collection methods will directly affect the results of the research. Therefore, more scientific data collection methods and a reasonable number of subject should be considered in the research of TAPs translation process. In this paper, the translation strategies and translation unit and so on are analyzed. At the same time, the selection of subjects and data collection types are discussed in order to through the processing of experimental data, to analyze the translator’s psychological processes. It’s hoped the research can provide certain reference for real translation study.
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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 | 0.058 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".