Using Computer-Scored Measures of Emotion and Style to Discriminate among Disputed and Undisputed Pauline and Non-Pauline Epistles
Bibliographic record
Abstract
The Dictionary of Affect in Language that allows measurement of Pleasantness, Activation, and Imagery in texts and a computer program that provides several additional stylistic measures were used to score samples from Disputed (n = 22 samples) and Undisputed (n = 40) Pauline epistles and from Other New Testament epistles (n= 16). All samples came from an English translation. Several significant mean differences were noted between samples from Disputed and Undisputed epistles. A discriminant function predicting Disputed or Undisputed authorship limited to five predictors was 85% successful in assigning samples to either category. The majority of samples from Other epistles were classified as Disputed, i.e., less likely to have been written by Paul. Undisputed Pauline samples were predicted to be those with lower Imagery, shorter words, less frequent words, greater repetitiveness, and greater Pleasantness. There were significant differences in patterns of word use (vocabulary) between Disputed and Undisputed samples.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".