The Analysis of Translated Hedges in Trump’s Political Speeches and Interviews
Bibliographic record
Abstract
This study tackles the analysis of translated hedges, in Trump’s political speeches and, interviews in the data, which have taken from three different political interviews of press conferences; that have conducted with U.S. President Donald Trump about coronavirus with their translations into Arabic. Therefore, the study has adopted Fraser’s classification of hedges and tries to apply it into the data. Moreover, the study has applied statistics to find out that illocutionary force hedges have widely used in Trump’s political speeches and interviews more than the propositional hedges according to Fraser’s classification of hedges. Thus, hedges can be considered as one of the most important linguistic phenomena because it can widely be used as a way of expressing points of view in political discourse. In addition to that, this linguistic phenomenon can be used by variety speakers of people in their daily life such as doctors, teachers, lawyers, but in particular politicians in their speeches, TV-interviews and press-conferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".