On the Strategies of Graduation Thesis Writing Teaching of Translation Major Undergraduates Based on Eco-Translatology
Bibliographic record
Abstract
Graduation thesis is an indispensible procedure for each undergraduate, which is crucial for successful graduation, employment, further study and even further development. However, due to most undergraduates’ ignorance of academic writing and the deficiency of current thesis writing course, thesis writing ability can hardly be enhanced and improved, accordingly, few good graduation theses can be produced among undergraduates. As a newly-launched major in recent a few years, translation major (BTI education, i.e. Bachelor of Translation and Interpretation) is also faced up with this problem. Eco-Translatology, a new theory combining ecology and translation studies, holds translation as adaptation and selection, which can also provide important enlightenment for thesis writing of translation majors. This paper, based on Eco-Translatology and current thesis writing course deficiency, aims to explore constructive strategies on how to give effective and efficient thesis writing course in hope of greatly improving thesis writing.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".