Scientific Models for Religious Knowledge: Is the Scientific Study of Religious Activity Compatible With a "Religious Epistemology"?
Bibliographic record
Abstract
Epistemologies of tested beliefs (knowledge claims) in scientific practice and non-tested yet faith-imbued beliefs (belief claims) in religious life are compared and contrasted. A study of models of rationality in contemporary philosophy of science and religion is completed with the purpose to assess possible compatibility systems in “science and religion” literature. Myths are re-contextualized in the modern scientific cosmos via the igmythicist conception of myths—myths are neither mere delusions nor reflections of an ontological reality for the gods, but myths are the application of meaning-enclaves enclosed in the world of natural human experience. It is argued that, if a compatibility system is successful in mapping shared epistemic territory between knowledge claims and belief claims, the compatibility system will be based on a theory of rationality which consistently tests knowledge claims and belief claims. While the cognitive values of a scientific epistemology provide an epistemic benchmark for testing many beliefs, the problem of constructing a “religious epistemology” in a modern, Western university is analyzed. Philosophical and theological benefits and limitations of the proposed “religious epistemology” are assessed and the place for a theory of rationality in religious life considered.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".