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Record W4319779061 · doi:10.4000/map.6204

Un coming-of-age métis à la française ?Lecture intersectionnelle de récits diasporiques en mouvement dans 35 Rhums (Claire Denis, 2008) et Khamsa (Karim Dridi, 2008)

2022· article· fr· W4319779061 on OpenAlexaboutno aff
Léonard Cortana, Leïla Tazir

Bibliographic record

VenueMise au point · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicLinguistic and Sociocultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Depuis le début des années 2000, de nombreux films français ont mis en scène des protagonistes jeunes métisses dans leur transition vers l’âge adulte. Ce processus et genre cinématographique appelé « coming-of-age » dans les pays anglophones n’a pas d’équivalent linguistique en France. Deux films sortis après l’élection de Nicolas Sarkozy en 2008, 35 Rhums de Claire Denis et Khamsa de Karim Dridi, s’approprient cette « transition » pour interroger l’impact des héritages postcoloniaux dans la société française. La mise en dialogue de ces deux films révèle des rapprochements dans l’expression de traumatismes intergénérationnels, la réalisation de rites de passage dans des lieux de mémoire et les difficultés des personnages à s’enraciner dans leur francité, qui s’expriment souvent dans le choix de s’exiler. L’angle intersectionnel permet une analyse de la construction des personnages, au-delà des identités de « race » et d’origine pour y inclure le genre, la classe et les habiletés corporelles. Les films deviennent des laboratoires de construction d’identités plurielles, qui rendent plus complexes les représentations stéréotypées des « jeunes de banlieue » dans l’espace public français.

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)0.003
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 score0.231
Threshold uncertainty score0.460

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0230.016
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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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