Measuring Nonreligion as Absence: Testing Various Approaches
Bibliographic record
Abstract
We examine the strengths and weaknesses of different “absence measures” scholars use to classify individuals as religious or nonreligious. Drawing on a novel dataset with data from eight countries (Argentina, Australia, Brazil, Canada, Finland, Norway, the UK, and the USA), we analyze how many people would be considered nonreligious based on four common measures: religious affiliation, religious service attendance, belief in a monotheistic god, and self-reported religiosity. We find that different measures lead to substantially different estimates of the number of nonreligious people in a country. The single measure that identifies the highest percentage of nonreligious people is never attending religious services, while the measure that identified the lowest percentage was those who report they are not at all religious. We also show that self-reported religiosity is a stronger predictor of attitudes toward religion than the other measures. Our findings suggest that scholars need to consider carefully the implications of using different measures of nonreligion, as this decision can have a meaningful impact on research findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".