“The Jews love numbers”: Steven L. Anderson, Christian Conspiracists, and the Spiritual Dimensions of Holocaust Denial
Bibliographic record
Abstract
From his pulpit at Faithful Word Baptist Church (Independent Fundamental Baptist) in Tempe, AZ, fundamentalist preacher Steven L. Anderson launches screeds against Catholics, LGBTQ people, evolutionary scientists, politicians, and anyone else who doesn't share his political, social, or theological views. Anderson publishes clips of his sermons on YouTube, where he has amassed a notable following. Teaming up with Paul Wittenberger of Framing the World, a small-time film company, Anderson produced a film about the connections between Christianity, Judaism, and Israel, entitled Marching to Zion (2015), which was laced with antisemitic stereotypes. Anderson followed Marching to Zion with an almost 40-minute YouTube video espousing Holocaust denial, entitled “Did the Holocaust Really Happen?” In this article, I analyze Anderson's Holocaust denial video in light of his theology, prior films, and connections to other Christian conspiracists, most notably Texe Marrs, I particularly show how Anderson frames the “Holocaust myth,” as he calls it, in light of a deeper spiritual warfare that negatively impacts the spread of Christianity.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".