Negative Other-Representation in American Political Speeches
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.).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".