A Study of Trump’s Inaugural Speech and Its Chinese Version from the Perspective of Transitivity
Bibliographic record
Abstract
In linguistics, Systemic Functional Linguistic School represented by Halliday has been having a great impact in the field of discourse analysis in the past 30 years and has become one of the important discourse analysis schools. As a significant part, transitivity reflects the ideational function in the systemic functional grammar and boasts a wide application in discourse analysis, such as in speeches. This paper has selected Trump’s inaugural speech and its Chinese version as a case study for sample and quantitative analysis of underlying textual features of political speeches from the perspective of Halliday’s transitivity, attempting to help readers get a better understanding of transitivity theory and features of political speeches, as well as consequently master the method of applying transitivity to political speeches. Furthermore, by way of a contrastive analysis of transitivity between Trump’s inaugural speech and its Chinese translation, it tries to delve into the translation strategies of political speeches, to guide practice translation of political speeches, to prove the applicability of transitivity in translation studies and to offer a creative angle for further researches.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".