Bibliographic record
Abstract
Wissenschaftliche Untersuchungen zum Neuen Testament of the Qumran scrolls are the protagonists of Sukenik's remark: the scribes.The purpose of this study is to explore the Qumran scrolls through the lens of individual scribes, specifically, the practices of individual scribes responsible for penning two or more of the manuscripts.3 It gathers a plethora of previously ungathered data on the handwriting, spelling practices, codicological features and literary content of individual scribes.It compares and contrasts this data with theories and models in the field that offer reflections on the unknown and enigmatic origins of the DSS.This study explores how the data on scribes both supports and challenges various aspects of theories in the field that accept a sectarian origin for the Qumran manuscripts.The study concludes by discussing what the work of one scribe in particular contributes to conceptions of sectarian, scholar scribes at Qumran. 4 Porten and Ada Yardeni, Textbook of Aramaic, Hebrew and Nabataean Documentary Texts from the Judaean Desert and Related Material.2 vols.(Jerusalem: Hebrew University, 2000).Ada Yardeni, Understanding the Alphabet of Dead Sea Scrolls: Development, Chronology, Dating (Jerusalem: Carta, 2014).Eibert Tigchelaar has offered the field a helpful account of the history of the palaeographic dating of the DSS.Eibert Tigchelaar, "Seventy Years of Palaeographic Dating of the Dead Sea Scrolls," in Sacred Texts and Disparate Interpretations: Qumran Manuscripts Seventy Years Later,
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".