Negative Other-Representation in American Political Speeches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study has two aims: First, to investigate the way knowledge has been expressed in relation to the negative representation of the two categories, namely, immigrants (especially illegal ones) and Syrian refugees, in two of Donald Trump’s pre- and post-presidential speeches. Second, to examine the local ideologies that can be identified in relation to the negative representation of the two categories in the selected data. Consequently, four extracts have been selected to be critically examined by means of adopting eight selected strategies out of Van Dijk’s fourteen Strategies of Critical Epistemic Discourse Analysis (2011b) in combination with Van Dijk’s Ideological Square (2011a). The results have shown a lack of credibility in many of the statements Trump has made in order to support his negative representation of the two categories. Besides, the two extracts taken from the selected post-presidential speech boldly reflect his discriminatory tendency towards the two categories. Thus, these two points lead to the conclusion that Trump’s negative representation of the two categories is actually out of the discriminatory ideology he adopts against them rather than a mere persuasive strategy to win the (2016) presidential elections of the United States of America (henceforth the U.S.).
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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 | 0.000 | 0.023 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it