Bibliographic record
Abstract
This is my first issue as the editor of Sociology of Religion. Before I briefly outline my goals and vision for the journal, I want to thank several people who have made this transition process a smooth and enjoyable experience. First and foremost, David Yamane and Bill Swatos have been incredibly supportive. During the past year, I have learned a tremendous amount from both of them. Collectively, we all owe them a great deal of gratitude for their service to the association and the journal. In his “Farewell” note published in the last issue, David provided a comprehensive overview of the editorial landscape at Sociology of Religion. I will not reiterate those details here. David also thanked a few people at Oxford University Press. I would like to follow his lead and underscore a sincere thanks to Cindy Gross (Production Editor), Simone Larche (ScholarOne specialist), Patricia Thomas (Executive Editor), and Rachel Mill (Assistant Marketing Manager). I have been extremely impressed with their outstanding level of responsiveness and professionalism. Their support has made the transition and the day-to-day editorial tasks so much easier. Our journal is in very good hands with the folks at OUP!
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.389 | 0.219 |
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".