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Record W2040722695 · doi:10.1163/157006410x486729

Sons and Lovers and the Mirage: Recognition, Melodrama and Psychoanalysis in Mahfūz’s al-Sarāb

2010· article· en· W2040722695 on OpenAlexaff
Philip Kennedy

Bibliographic record

VenueJournal of Arabic Literature · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsYork University
Fundersnot available
KeywordsNarrativeLiteratureAmbiguityCertaintyMeaning (existential)PhilosophyClosure (psychology)PsychoanalysisArtAestheticsHistoryEpistemologyPsychologyLawLinguistics

Abstract

fetched live from OpenAlex

Abstract This essay provides a commentary on one of the less celebrated novels of Najīb Mahfū z,al-Sarāb (The Mirage), one of a series of tragic melodramas he wrote in the 1940s. Uniquely in Mahfūz’s oeuvre, this novel explores an “Oedipal” relationship between the narrator/protagonist and his mother; it is a relationship that blights the man’s married life. What Aristotle identified as anagnorisis—a movement towards startling disclosure—is a common feature of Oedipal narratives; the anagnorisis in Sophocles’s Oedipus Rex being the most famous example (and Aristotle’s favorite instance of tragic disclosure). In general, and across genres, anagnorisis provides narratives with familiar, sometimes comforting, shape; it can also provide an interpretive portal onto the meaning and values revealed by a narrative. It tends to provide closure, especially in melodrama, and often purveys a sense of certainty (though in contemporary or experimental fiction that complacency is often misplaced: incertitude, ambiguity and difficult knowledge are just as often revealed). The commentary below concentrates on three aspects of al-Sarāb alluded to and the relationship between them: recognition, psychoanalysis and melodrama.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.021
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.256
Teacher spread0.250 · 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 designTheoretical or conceptual
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
Published2010
Admission routes1
Has abstractyes

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