A Radical Shift to a Profound and Rigorous Investigation in Political Discourse: An Integrated Approach
Bibliographic record
Abstract
Drawing on overarching methodological frameworks of Hallidayan grammatical metaphor, Fairclough’s perspective on critical discourse analysis and rhetoric, this study attempts to posit a novel, integrated and practical approach to political, the media, advertisement and other discourses. To this end and based on the proposed approach, it aims to critically and eclectically exemplify and dissect three speeches delivered by Mr. Barack Obama, former president of the US, to first manifest the integrated approach practicality and adeptness through analysis; then by virtue of analysis to unveil how language is manipulated and distorted by orators in order to convey seamlessly intended messages and political creeds to the audience. Surveying recent annals of literature, to date no one has conducted an integrated study applying these disciplines in an individual paper and this study as a trial one can be useful for upcoming research. The analysis depicts practicality and efficiency of the integrated approach and displays that the speeches abound with nominalizations, modal verbs, parallelisms and antitheses. Furthermore, there are some three-part listing, the use of passivization, quotations and modality metaphors. Therefore, a tendency to utilize more nominalizations, parallelism and other devices by the speaker can be a fundamental reason for making his political language more powerful, impressive, persuasive and ambiguous as well.
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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 | 0.056 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.006 | 0.103 |
| Scholarly communication | 0.028 | 0.047 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".