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Record W4229445659 · doi:10.24847/v9i12022.318

"Interstitial Spaces and Sites of Struggle": Displacement, Identity, and Belonging in Contemporary French Accented Cinema

2022· article· en· W4229445659 on OpenAlexaff
Sheila Petty

Bibliographic record

VenueMashriq & Mahjar Journal of Middle East and North African Migration Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMovie theaterPerformative utteranceIdentity (music)Displacement (psychology)ColonialismRefugeeArtEthnologySociologyHistoryGender studiesHumanitiesArt historyAestheticsArchaeology

Abstract

fetched live from OpenAlex

This essay focuses on three “accented cinema” films that creatively blend the autobiographical, performative, fictional, and documentary to interpret how identities are created at the intersection of displacement and belonging and are shaped by selective and multiple affinities to colonial cultures, ancestral cultures, and adopted host cultures. The films represent historical “moments” and follow a historical chronology, highlighting the permanency of being displaced and displacement in France, the said Republic of Equality. Watani: un monde sans mal (Watani: A World Without Evil, 1998) by Med Hondo depicts several racist incidents in Paris, including the Sans-Papiers Affair of 1996; La Marche (The Marchers, 2013) by Nabil Ben Yadir recreates the 1983 Marseilles to Paris “Marche Pour l’Egalité et Contre le Racisme”; and Paris Stalingrad (2019) by Hind Meddeb and Thim Naccachechronicles refugees struggling to make a home for themselves in street camps in the Stalingrad district of Paris.

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)0.002
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.010
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.097
GPT teacher head0.306
Teacher spread0.210 · 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
Published2022
Admission routes1
Has abstractyes

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