The Distinct Emotional Flavor of Gnostic Writings from the Early Christian Era
Bibliographic record
Abstract
More than 500,000 scored words in 83 documents were used to conclude that it is possible to identify the source of documents (proto-orthodox Christian versus early Gnostic) on the basis of the emotions underlying the words. Twenty-seven New Testament works and seven Gnostic documents (including the gospels of Thomas, Judas, and Mary [Magdalene]) were scored with the Dictionary of Affect in Language. Patterns of emotional word use focusing on eight types of extreme emotional words were employed in a discriminant function analysis to predict source. Prediction was highly successful (canonical r = .81, 97% correct identification of source). When the discriminant function was tested with more than 30 additional Gnostic and Christian works including a variety of translations and some wisdom books, it correctly classified all of them. The majority of the predictive power of the function (97% of all correct categorizations, 70% of the canonical r2) was associated with the preferential presence of passive and passive/pleasant words in Gnostic documents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".