Problematizing religious truth: Implications for public education
Bibliographic record
Abstract
The question motivating this paper is whether or not there can be standards governing the evaluation of truth claims in religion. In other areas of study — such as physics, math, history, and even value‐laden realms like morality — there is some widespread agreement as to what constitutes good thinking. If such a standard existed in religion, then our approach to teaching religion would need to change. This paper, however, is a prelude to examining such a question. In it, we briefly explore whether or not religion should even be included in public education. After concluding that it should be, we then look at whether we should pursue questions of truth in discussing religion or whether truth should be bracketed. If matters of truth are bracketed, what is lost? If questions of truth are pursued in our public school classrooms, what standards of evaluation should be applied to them?
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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 | 0.057 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.064 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".