A Discourse-Historical Approach to Populism in the Right-Wing Discourse on Immigration
Bibliographic record
Abstract
This study is concerned with investigating the implications of the new nationalist and populist discourse of the far right-wing movements to immigration in different Arab countries, with a focus on Egypt, Lebanon, and Jordan. For this purpose, the study is based on a corpus of different genres, including political speeches, newspaper articles, as well as social media posts and comics. Critical Discourse Analysis (CDA) is used in order to explore speakers’ ideologies and how rhetoric and discursive strategies are employed to influence public opinion and persuade citizens about certain views and policies and even prompt them to take the desired action. Results indicate that the new nationalist and populist discourse adopted by different politicians and far right-wing parties and movements have negative impact on the rights of migrants and refugees in Arab countries. Migrants and refugees are used as scapegoats for political gains. They are blamed for all social, economic, and political challenges and crises these countries are suffering today. Right-wing movements are embedding some hidden ideologies in their political discourse that are related to the hate and rejection of migrants and refugees. It can also be concluded that the increasing popularity of anti-immigration movements and radical right-wing political leaders hint at the influence of the nationalist and populist discourse on the public opinion in their countries. Populist discourse has led to fear and rejection of the “Other”, and even to racist acts and xenophobia.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".