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!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".