To Err in their Ways: The Attribution Biases of the New Atheists
Bibliographic record
Abstract
The term ‘‘new atheism’’ has been given to the recent barrage of anti-religion and anti-God books written by Richard Dawkins (2006), Sam Harris (2004, 2008), Christopher Hitchens (2007), Daniel Dennett (2006), and others. This paper contends that one of the fundamental arguments put forth by the new atheists — that religion poisons everything or that religion is responsible for much of the evil in the world — falls victim to one of the best established theories of interpersonal and intergroup relations in social psychology: the fundamental attribution error. Insights gleaned from social psychology are especially useful for critiquing the new atheism. Instead of simply arguing that the new atheists ‘‘over-generalize,’’ social psychological studies on the nature of individual and group attribution provide the tools needed to launch a more substantive critique.
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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 | 0.034 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".