“I do consider some of them to be racially white but they shall not be White”: Terrorist Manifestos and the Construction of White Masculinity
Bibliographic record
Abstract
I investigate the public manifestos of Anders Brevik (2011); Patrick Crusius (2019); Payton Gendron (2022); Frazier Glenn Miller, Jr. (1999, 2002); Elliot Rodger (2014); Dylann Roof (2015), and; Brendon Tarrant (2019). I analyze these documents not as reflections of static identities or ideologies but as discursive mechanism that helps reproduce an ideal White masculinity as an ongoing accomplishment via (1) of defining the problem and assignment of blame in the essentialization of difference; (2) the proposal of prognostic strategies and tactics toward exercising dominance, and; (3) a call to arms through an appeal to emotional virtue. In so doing, I confront popular, media, and scholarly tendencies to psychologize and minimize the social dynamics within contemporary assemblages of White masculinity. I also show how some of the central themes within these manifestos are, despite being labeled as radical, extreme, and sporadic, resonate with widespread, banal, and largely accepted attitudes held by many White Americans today. I then turn to why these manifestos evidence the ongoing accomplishment of crisis white masculinity.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".