Why Don't Americans Accept Evolution as Much as People in Peer Nations Do? A Theory (Reinforced Theistic Manifest Destiny) and Some Pertinent Evidence
Bibliographic record
Abstract
Abstract Prior speculations about why Americans don't embrace evolution — as much as comparable nations’ residents do — are generally dated and not well assessed. Reinforced Theistic Manifest Destiny (RTMD), introduced in this chapter, represents a more obviously predictive theory that focuses on spiritually-linked feedback regarding the U.S.’s military (and industrial) prowess. RTMD joins analyses of (a) individuals’ motivations, emotions, and epistemologies, with (b) intra- and inter-national historical narratives. Many of RTMD’s empirical hypotheses are discussed and from the U.S. and Canada. The North American findings largely cohere with the relevant set of RTMD’s predictions, given the variety of associations observed among beliefs regarding afterlife, theism, nationalism, global warming, and the origins of species. These encouraging experimental and survey studies offer further implications regarding how evolution might be better conveyed in both formal and informal settings — and why we should teach evolution in the first place (e.g., preserving Earth’s biosphere).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".