Exploring the Challenges and Potentialities of the Database of Religious History for Cognitive Historiography
Bibliographic record
Abstract
This article explores the potential impact and contribution of the Database of Religious History (DRH) project within the field of Cognitive Historiography. The DRH aims to bring together, in a systematic and open-access format, data on religious groups from across the globe and throughout history. By utilizing robust, open-source technologies and best-practice software principles, the DRH constitutes a novel and innovative approach to historical and cultural studies. As a contribution to the scientific study of both religion and history, the DRH offers data amenable to statistical analyses, thus providing tools for assessing diachronic cultural innovation and adaptation, the testing of grand narrative theories of religious change, and for enriching and revitalizing traditional fields such as comparative religions, history of religion(s), and anthropology of religion. In this article we explore the methods employed in collecting and digitizing historical data, identify our unit of analysis, outline the challenges of recruiting historians of various fields, and highlight the DRH’s methodological potential for both Religious Studies and Cognitive Historiography.
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 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 | 0.063 | 0.176 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".