Cultural Genocide, Mass Immigration, and the Kalergi Plan: Conceptualizations of Race in White Identity Politics Online
Bibliographic record
Abstract
This study aims to clarify ambiguities surrounding the understanding of race in white identity politics and how these ambiguities are reflected in online discourse. Grounded in the framework of critical race studies, we constructed a comprehensive race typology that we used to unpack the multifaceted conceptions of race in digital discourse on Twitter. Using a combination of Tableau, NVivo, and manual coding, we examined the prevalence of four conceptualizations of race (Biological, Cultural, Nationalist, and Pan-Nationalist) in data collected from three Twitter hashtags (#whitegenocide, #kalergiplan, and #antiwhite). We conclude that race does not stand out as one coherent system in the analyzed data but as an amalgam of divergent racial interpretations. Notably, the Cultural conceptualization of race is the most predominant, followed closely by the Pan-Nationalist perspective of white identity. Our investigation also explores the palpable anxiety surrounding the perceived erosion of the white race within white identity politics. This apprehension is prominently articulated as "white grievances" and through a gendered understanding wherein white women assume a pivotal role in both propagating the white race and in acting as a vulnerable "access point" within the white racial framework.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".