Bibliographic record
Abstract
For seventeen years, at 1:00 p.m. on the first Thursday after Labour Day, I walked into the main lecture room at the College of Emmanuel & St. Chad in Saskatoon, to find waiting for me the thirty or so students who had enroled in my two-semester course, "Introduction to the New Testament and Its World." Invariably, it was a mixed group. Usually the class was evenly split between theological students, registered through the Anglican or the United Church Seminary, and university students, registered through the Department of Religious Studies. For some of them the New Testament was familiar territory; others were hard put to tell the difference between an epistle and an apostle. Included in the latter group were students such as the one who, early in the semester, made some comment about the Pharisees and the "Seducees" (the accent fell on the second syllable!), and another who made reference in a paper to that less well-known disciple of Jesus, Judas Escargot. That these howlers were both produced by seminary students should serve to indicate that there was no necessary correlation between the degree for which a student was studying and his or her degree of biblical literacy. (Of course, the students present here this evening are not the kind to produce such blunderful wonders.)
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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".