Testing and Disrupting Ontologies: Using the Database of Religious History as a Pedagogical Tool
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.
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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 | 0.037 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.032 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".