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Record W2514862456 · doi:10.1057/9780230596054_8

The Lower Depths

2000· article· en· W2514862456 on OpenAlexaff
Moshe Aberbach, David Aberbach

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMidrashJudaismPower (physics)Roman EmpireCreativityLiteratureHistoryEmpireHebrewOriginalityPhilosophyLawArtClassicsTheologyAncient historyPolitical science

Abstract

fetched live from OpenAlex

At the heart of Hebrew creativity in the Roman empire after the Jewish wars are brief brilliant fragments of Midrash. Midrash was to some Jews in the Roman empire not unlike jazz to American Blacks: the creativity of a minority defeated and discriminated against, bent but not broken, zealously preserving an inner cultural and religious space where it was free and imagination ruled. Midrash, unlike halakhah, openly describes the tragedies and degradations of the Jews in the years 66–138 CE. It is impossible to establish beyond doubt the degree to which these midrashim from the tannaitic era are historically accurate (see the Note on Texts, pp. 19–20). They appear in works edited or written long after the periods in which they are set. Yet there is little doubt that aggadot are based on real events. Also, it is rarely possible to prove decisively that they are apocryphal. To Goodman (1983), for example, these aggadot stand out as they ‘display a rare feel for history in what are mostly ahistorical writings [and] show remarkable acquaintance with the tactics of Roman armies in quelling revolts’ (pp. 136, 137). As literary fragments, too, they have unusual originality and power.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.086
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0110.008
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0860.018

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 head0.021
GPT teacher head0.205
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2000
Admission routes1
Has abstractyes

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