Bibliographic record
Abstract
About the book: \nThe power of modernity to secularise has been a foundational idea of the western world. Both social science and church history understood that the Christian religion from 1750 was deeply vulnerable to industrial urbanisation and the Enlightenment. But as evidence mounts that countries of the European world experienced secularising forces in different ways at different periods, the timing and causes of de-Christianisation are now widely seen as far from straightforward. \nSecularisation in the Christian World brings together leading scholars in the social history of religion and the sociology of religion to explore what we know about the decline of organised Christianity in Britain, Europe, the United States, Canada and Australia. The chapters tackle different strands, themes, comparisons and territories to demonstrate the diversity of approach, thinking and evidence that has emerged in the last 30 years of scholarship into the religious past and present. The volume includes both new research and essays of theoretical reflection by the most eminent academics. It highlights historians and sociologists in both agreement and dispute. With contributors from eight countries, the volume also brings together many nations for the first consolidated international consideration of recent themes in de-Christianisation. With church historians and cultural historians, and religious sociologists and sociologists of the godless society, this book provides a state-of-the-art guide to secularisation studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".