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Hidden Allusion in the Finale of<i>Middlemarch</i>: George Eliot and the Jewish Myth of the<i>Lamed Vov</i>

2018· article· en· W2888775372 on OpenAlexaff
Judith Adler

Bibliographic record

VenueGeorge Eliot - George Henry Lewes Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAllusionMythologyJudaismLiteratureGeorge (robot)Interpretation (philosophy)PhilosophyHistoryArtArt historyTheology

Abstract

fetched live from OpenAlex

Abstract This article argues that the famous concluding paragraph of George Eliot's Middlemarch contains a hitherto unrecognized cryptic allusion to a Jewish myth newly popular in her time: lamed vov, a specific “number” of “hidden” righteous persons believed to “channel” flows of divine compassion, thereby preserving and slowly improving the human world. Once recognized, this cryptic allusion has implications for the interpretation of the novel, casts new light upon the philosophical problems with which Eliot wrestled and upon her ideal of Realism, as well as upon her changing relationship to Jews and Jewish tradition. Eliot's treatment of Jewish material is commonly exclusively identified with her last works, especially Daniel Deronda. But Middlemarch marks an earlier, discreetly veiled phase of that relationship, before Eliot came publicly “out” with her philo-Semitism and determination to serve as a culture-broker. This article offers an account of Eliot's sources, of turning points in her relationship to Jews and Jewish tradition, and of the important place of Middlemarch in that development.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.021
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.305
Teacher spread0.270 · 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
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

Citations0
Published2018
Admission routes1
Has abstractyes

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