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Record W3120541784

Daniel Duval. L’Ombre des châteaux Film, Fiction, 1978

2020· article· fr· W3120541784 on OpenAlexaboutno aff
Bella Lehmann-Berdugo

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Sous le ciel bas du nord de la France, dans la gadoue, la famille Capello (emigres italiens) vit au fond d’une baraque exigue et sombre. Le decor du film1 est plante. Il y a le pere, morose, taiseux, qui teste des ballons du matin au soir. Chaque petit « clac » resonne comme un cri bref. A ses cotes, la mere enfile des chapelets a revendre. Petits travaux mecaniques pour joindre les deux bouts. Pour « les vieux » c’est deja « cuit ». Leurs fils Luigi (Philippe Leotard) et Rico vaquent a d’improbables besognes et revent surtout de partir aux Republiques du Canada d’Amerique. Leur sœur, Fanny, semble flotter autour de tout ca. Mais elle est placee dans un centre de reeducation apres avoir ete prise en flagrant delit de vol.  Châteaux au Canada : l’argent, la reussite, avoir le respect des autres,… est-ce donc pour ceux qui sont nes du mauvais cote ? Des la premiere scene on sent un poids peser sur les personnages. Mais aussi la tendresse de l’auteur pour leurs vies invisibles, pour les efforts demesures qu’ils font pour s’en sortir. Chez ces gens-la, on n’a pas les mots pour dire la peine, la joie, ni ceux pour se defendre. Pour faire sortir leur sœur du mauvais pas, ils se rendent au proces (et non les parents). Ils se feront rouler par un avocat incompetent et sans scrupule (scene emblematique ou les freres s’habillent bien pour le tribunal, ou ils interviennent lors du verdict, dans leur langage a eux). La sequence du parloir est tout en retenue. Au-dela des murs, la fratrie

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.122
Threshold uncertainty score0.242

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 studies0.0080.003
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0470.009

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 head0.328
GPT teacher head0.301
Teacher spread0.026 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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