Testing and Disrupting Ontologies: Using the Database of Religious History as a Pedagogical Tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an age of “Big Data” the study of the history and archaeology of religion faces an exponentially increasing quantity and range of data and scholarly interpretation. For the student and scholar alike, new tools that allow for efficient and accurate inquiry are a necessity. Here, the open-access and digital Database of Religious History (DRH) is presented as one such tool that addresses this need and is well suited for use in the classroom. In this article, we present the basic structure of the database along with a demonstration of its potential use. Following a thematic inquiry into questions concerning “high gods”, individual disciplinary-specific case studies examine applications to particular contexts across time and space. These case studies demonstrate the ways in which the DRH can test and disrupt ontologies through its ability to efficiently cross traditional disciplinary boundaries.
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.
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 | 0.001 | 0.000 |
| 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 it