Bibliographic record
Abstract
This chapter presents some examples of the vast variety of Jesus-related objects, discusses their functions, and considers contemporary attitudes towards them. The ubiquity of Jesus makes the catalogue of Jesus-related objects extensive, including automated clocks, bells, liturgical garments, and funeral shrouds. In particular, relics from Jesus's body (including his blood, his foreskin, his beard) and relics once in contact with his body (wood from the manger, clothing he wore, his sword) had complex histories, often crossing between the Muslim and Christian worlds, especially in their use as gifts or in their accumulation by enthusiastic collectors. Beyond relics, some more ordinary objects in the Jesus cult served as amulets infused with power through their design and application. Underlying the technology behind all these objects were numbers, oriented either to the deep ken (round in beautiful ways) or the plain (precisely measuring some aspect of the human Jesus). Doubts often clustered around plain-ken criticisms: The cross cult developed chronologically in time, and “True Cross” fragments combined far exceeded the size of the cross itself—an impossibility given the plain-ken rules of spacetime. The plain ken prevented the multiplication of relics, and demoted circumcision and crucifixion from powerful symbols to everyday first-century customs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".