Bibliographic record
Abstract
iv ABSTRACT Differentiating Satan’s Many Faces: A Reception History Approach Phillip D. Haskell McMaster Divinity College Hamilton, Ontario Doctor of Philosophy (Christian Theology), 2024 The study of Satan has been approached in a variety of ways with some harmonizing texts to construct a singular description of the Satan figure, others have tried to trace a singular development of Satan over time, and others still acknowledge disparate presentations of Satan throughout the Second Temple period but lacking from these attempts is one which considers whether multiple ideas of Satan have developed over time. This dissertation reconstructs a reception-history of the Satan figure by tracing the many permutations of a leading figure of evil throughout the Old Testament, Second Temple writings, and the New Testament. This process demonstrates that there exist distinct conceptions of the Satan figure and that these conceptions have formed developing traditions which themselves show variance in key understandings. These differing notions of the Satan figure are contrasted against the writings of New Testament authors to show how of their understandings of Satan may have been influenced by earlier traditions. A final section of this study organizes the writings of the Old Testament, Second Temple period, and New Testament into stratified layers based on dating so that their ideas can be read considering historical events of their time. This comparison helps to show how prominent events in history have stimulated thinking regarding the Satan figure over the course of time and that New Testament authors had not yet refined these different ideas into a singular depiction.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".