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Record W1124596762 · doi:10.3138/jrpc.29.3.1367

Female Resistance in <i>The Legend of Sigh</i>

2017· article· en· W1124596762 on OpenAlexvenueno aff
Sarah AbdulRazak

Bibliographic record

VenueJournal of Religion and Popular Culture · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsnot available
Fundersnot available
KeywordsLegendResistance (ecology)Power (physics)Movie theaterScrutinySubjectivityGender studiesSociologyMythologyIdeologyIndividuationIdentity (music)AestheticsPsychoanalysisPsychologyLiteratureArtPoliticsPolitical sciencePhilosophyLawEpistemology

Abstract

fetched live from OpenAlex

The last two decades following the Islamic Cultural Revolution have led filmmakers of the New Iranian Cinema to disguise social commentary in film through myths and metaphors. This study highlights the very beginnings of women’s changing roles from being sexual objects of minor interest to becoming subjects of prominent scrutiny in a feminist filmmaker’s film, The Legend of Sigh. Five parallel but alternating stories bring together the dilemmas of five women from different strata culminating in the binding paradox of their lives. It is within this paradox that the role of Sigh, as a duality of the masculine and feminine, is examined as the touch point of the subversive and collaborative layers of the film. A character analysis determines how female representations are structured across power discourses, particularly wielding results of how the technology of power and state ideology shapes female subjectivity by transforming the female voice into sites of resistance. Resistant measures are cited as cries for either initiation into individuation or liberty from the patriarchal norm. The causal motivation behind that resistance is what links the women and their contexts together within the plot. They are all equally either abused or abandoned by the norm in power. Despite the struggle to empower their identity through the maternal guise of Sigh, his paternal guise succeeds in elevating the female voice by confining it to the space of the other.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.929
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.314
Teacher spread0.289 · 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 teacher head, 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

Citations1
Published2017
Admission routes1
Has abstractyes

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