Socio-economic Issues Related to Immigrants in American Political and Election Discourses
Bibliographic record
Abstract
Nativism is conceptually different from xenophobia. A xenophobe is not necessarily a nativist. Nativism can broadly mean binarism and racism together. This study traces the history of American politicians’ nativist rhetoric and its reflection on the life of the immigrants. In the United States, nativism has largely been a part of the leaders’ political and cultural agendas and motivated the Black-White racial binarism. Moreover, nativism continues to second this binarism and secure it from criticism by projecting it as a high level of nationalism. This paper investigates, firstly, how the nativist speech influences common man; and secondly, how the life of the immigrants is affected by this discourse. This study contrasts with many dominant theories, which hypothesize that American political discourse is controlled by the elites and directed by their nativist agendas. This study, however, finds that American political discourse is subject to the nativist pride of common white citizens who share this anima with the elites.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".