Ecological Discourse Analysis of the English Translations of Song of Peach Blossom Land from the Perspective of Transitivity
Bibliographic record
Abstract
In light of the degradation of the ecological environment, linguists have integrated ecology with linguistics, giving rise to the emergence of ecolinguistics. Ecological Discourse Analysis (EDA), as a form of discourse analysis, serves as an analytical approach within the field of ecolinguistics. Considering that Systemic Functional Linguistics (SFL) can be applied in analyzing ecological discourses and acknowledging the existing differences between Chinese and English languages, this paper conducts an EDA on both Chinese and English translations of “Song of Peach Blossom Land” from a transitivity perspective, aiming to present disparities in translation skills and accuracy. The results reveal that material processes account for over 50% of processes in all three texts and are consistently observed throughout them. Natural ecology is portrayed through depictions of the peach blossom land’s scenery; social ecology is presented by describing the social background and friendly relationship between the fisherman and villagers; spiritual ecology is embodied by his decision to leave his hometown but ultimately return to this land again. Furthermore, disparities in translation skills and accuracy exist within the Chinese-English translation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".