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Record W4384559447 · doi:10.1080/25785273.2023.2231777

West as home in Ruba Nadda’s films

2023· article· en· W4384559447 on OpenAlexaffabout
May Telmissany

Bibliographic record

VenueTransnational Screens · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFilm directorHomelandMovie theaterMainstreamRomanceAnimePoliticsComedyMedia studiesPoeticsSociologyAestheticsHistoryGender studiesArtArt historyLiteraturePolitical sciencePoetryLawPhilosophy

Abstract

fetched live from OpenAlex

In this essay, I draw on the concepts of accented/transnational cinemas to discuss four feature films directed by Canadian Syrian filmmaker and TV director Ruba Nadda. Through Sabah (2005); Cairo Time (2009); Inescapable (2012); and October Gale (2014), the viewers witness the reconstruction of the West as home from the standpoint of a Canadian-born Toronto-based woman filmmaker of Arab descent. Despite Nadda’s constant yearning to her Arab origins, one can claim that borders between Arab and non-Arab are not always blurred in her films. Instead, cultural bridges are built across borders to transcend the traditional poetics of exile/immigration and to overcome politics of cultural despair triggered by binaries such as East and West, homeland and hostland, national and transnational belonging, etc. By focusing on issues of homeness, integration, religious difference, and cultural recognition, on the one hand, and questions of home-returning and home-reconstruction, on the other hand, I argue that no matter where Nadda’s characters dwell, they belong to the Western set of values, and they represent each a facet of her multiple identities, chief among them Western individuality. I also investigate the filmmaker’s gender-based approach to mainstream cinematic genres such as romantic comedy and thriller and her interest in these genres as a TV director as well.

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 categoriesInsufficient payload (model declined to judge)
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 score0.882
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.323
Teacher spread0.287 · 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.

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
Published2023
Admission routes2
Has abstractyes

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