Bibliographic record
Abstract
The exponential growth of information available in the world today means that books and articles run the risk of being obsolete shortly after they appear.It's not a new reality, just a reality that has become all the more accentuated, largely because of the arrival of the Internet in the early 1990s.I thought that my "gods series" of books did a reasonably good job of keeping up with trends and literature when they appeared in 1987 (Fragmented Gods), 1993 (Unknown Gods), and 2001 (Restless Gods).But the birth of Beyond the Gods and Back in 2011 took place at a time when information was exploding at a remarkable rate.In the six years since its release, I have clarified and refined my thinking on religious polarization.I have also generated and been exposed to much new data.As a result, while Beyond the Gods and Back is the informing backbone to this book -as reflected in the first two chapters -not much else remains the same.This book benefits from the availability of considerable new data, both global and Canadian in scope.The two key players for me in recent years have been the Pew Research Center based in Washington, DC, and the Angus Reid Institute in Canada.I am immensely indebted to both, along with Andrew Grenville, my colleague and friend with the Vision Critical research division of the Maru Group, who has played a central role in helping me to generate considerable new survey data.This book has also benefited from feedback from colleagues and students who used its predecessor as a text.I want to single out Joel
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.605 | 0.446 |
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".