Corpus Based Study of Personal Pronoun’s Rhetoric in Obama’s and Xi Jinping’s Diplomatic Discourse
Bibliographic record
Abstract
This paper examines how first personal pronouns in English aid president Obama and president Xi Jinping to speak persuasively on international platforms. Drawing on four speeches, this paper explores the frequency of first person pronouns realized in both singular and plural forms and analyzes, within a framework of Critical Discourse Analyses (CDA), how these pronouns are exploited using modal verbs and tenses to attain and sustain rhetorical appeal. This paper found out that Obama deploys personal pronouns selectively with more I-pronouns realized in his speech in Africa and more we-pronouns realized in his speech in Europe, has a bias towards modal verbs that highlight ability and intention: can and will, and prefers the future tense. On the other hand, this paper found out that Xi deploys both the I-pronouns and the We-pronouns equally in his speeches in both Africa and Europe, has an inclination towards we-pronouns in his diplomatic discourse, a bias towards modal verbs highlighting necessity: should and need, and prefers the future tense.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".