Why Truth Matters for the Study of Religion: A Defense of a Truth-Conditional Semantics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Truth-conditional semantics holds that the meaning of a linguistic expression is a function of the conditions under which it would be true. This seems to require limiting meaningfulness to linguistic phenomena for which the question of truth or falsity is relevant. Criticisms have been raised that there are vast swatches of meaningful language that are simply not truth-related, with religion representing a particularly rich and prevalent source. I argue that if the concept of truth as used in a truth-conditional semantics is understood in ways other than correspondence to fact, there are suitable reformulations of a truth-conditional semantics that may be appropriate for understanding religion. I further argue that these reformulations offer considerable methodological advantages to the scholar of religion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it