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Record W2085871218 · doi:10.2466/pr0.102.1.213-234

The Distinct Emotional Flavor of Gnostic Writings from the Early Christian Era

2008· article· en· W2085871218 on OpenAlexaff
Cynthia Whissell

Bibliographic record

VenuePsychological Reports · 2008
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPsychologyNew TestamentIdentification (biology)Function (biology)Discriminant function analysisAffect (linguistics)Variety (cybernetics)Word (group theory)LinguisticsLiteraturePhilosophyArtificial intelligenceCommunicationArtComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.046
GPT teacher head0.338
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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