Bibliographic record
Abstract
One way of defining or describing a plot is through its emotional structure. This article examines the emotional structure of the gospels of Matthew, Mark, Luke, and John in a modern English translation (WEB). Measures of emotion are based on quantitative sentiment analysis (Dictionary of Affect in Language). A common plot is identified for all gospels, modeled with a regression analysis (p<.001), and described in terms of the relationship of emotion to content across time. The plot opens on an emotionally positive note. Emotions become increasingly unpleasant as Jesus meets with resistance from religious authorities while engaging in his ministry. Emotions then become more pleasant as Jesus completes his pre-Judean ministry, experiences the Transfiguration, and enters Jerusalem in triumph. After this, emotions become increasingly unpleasant again, leading to the low of the crucifixion. A turn to more pleasant emotions characterizes the resurrection. In a separate analysis it was noted that segments of the gospels presented as spoken by Jesus were more pleasant than remaining materials (p<.001): however, they did not vary emotionally in accordance with the plot (p>.20), but remained relatively stable across time.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".