Christian Identity Theology: Religious Motivation & Violence
Bibliographic record
Abstract
From the Oklahoma City Bombing to the recent mass shooting of a Colorado Springs Planned Parenthood, conversations on Christian motivations of terror are becoming more prevalent. In his book, Terror in the Mind of God, Mark Juergensmeyer proposes five characteristics for explaining religious violence: (1) violence as performative and symbolic, (2) violence as part of a cosmic war, (3) violence is sacrificial, (4) enemies are ‘satanized’ or dehumanized, and (5) violence is committed to empower the marginalized. The Christian Identity movement is characterized by performative violence, cosmic war, satanized enemies, and violence to empower the marginalized. Yet, the Christian Identity movement does not fit the characteristic of sacrificial violence because they are trying to preserve the white race and thus do not want to kill their members. This paper will expand on Juergensmeyer’s model of five characteristics in Terror in the Mind of God by examining the Aryan Nations’ worldview under the Christian Identity movement and how it fits into the framework that Mark Juergensmeyer has created. This finding suggests that Mark Juergensmeyer’s explanatory devices cannot be applied to every militant religious group.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".