Bibliographic record
Abstract
It is not easy to define religiosity and the extent to which it represents a single unique phenomenon not reducible to more basic psychological processes such as meaning making. Saroglou (2010) proposed that the content of religiosity includes believing, bonding, behaving, and belonging. In his meta-analysis of the associations between religiosity and personality traits, he categorized the various measures or religiosity used in various studies as (1) religiosity involving beliefs and practices in relation to a transcendent being within an established tradition or group, (2) spirituality or a questioning faith independent of religious groups, and (3) fundamentalism consisting of authoritarian and dogmatic beliefs and practices. Measures from these three categories correlate modestly with agreeableness and conscientiousness. Honesty-humility also correlates modestly with measures of religiosity similar to the first category. Openness to experience correlates modestly, and in a positive direction to spirituality but negatively to fundamentalism.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".