Prophetic Scribalism: A Semantic, Textual and Hypertextual Study of the Serek Texts
Bibliographic record
Abstract
This thesis challenges the position that the serek texts are primarily prescriptive and legal, as they have been customarily defined. It argues that the term serek should be reconceptualized according to descriptive analysis, with the purpose of creating what C. Newsom terms a ‘Gestalt structure.’ In order to achieve this, four serek texts (M, S, Sa, and D) will be analyzed at three literary levels—semantic, textual and hypertextual—explaining how the elements at these levels interact as cohesive wholes, thus serving to create a more complete picture of this group of texts as a literary unity. Thus, while the separate, constituent semantic, textual and hypertextual parts must be analysed as separate elements, the fundamental questions posed regarding these elements will be different in a Gestalt paradigm as compared to a traditional, definitional analysis. Going from the micro to the macro, the first chapter will look at the serek texts through the ‘microscope’ of close philological analysis, examining how the term serek functions atomistically within the Dead Sea Scrolls. Building upon these results, the second chapter will more broadly analyse the structure, themes and narrative apparent in the serek texts, thus creating a fuller understanding of how the serek texts relate to one another and respond to circumstances in community life. Finally, the last chapter seeks yet more broadly to understand the serek texts in the wider literary milieu of the Second Temple Period. Here, a scribal technique present in the serek texts will be compared to a similar technique used in the Book of Isaiah—arguably the most important prophetic work for the Qumran sectarians.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".