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Record W7157611171 · doi:10.7202/1124602ar

Du regret des mets perdus en exil

2025· article· fr· W7157611171 on OpenAlexvenueno aff
Emma Barrett Fiedler

Bibliographic record

VenueAnthropologie et Sociétés · 2025
Typearticle
Languagefr
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Cultural environmentRegretDissimilation

Abstract

fetched live from OpenAlex

Cet article examine les raisons du lien consubstantiel existant entre l’élaboration nostalgique en contexte migratoire et l’attachement à la nourriture. Les cinq sens sont convoqués pour rendre compte de la connivence charnelle qui lie le souvenir de l’exilé à son lieu de départ, à ses paysages, son climat, sa luminosité, ses odeurs, ses bruits familiers, son esthétique particulière et ses habitudes alimentaires : la nostalgie se manifeste d’abord par la sensorialité, une « sensualité » organique faisant appel au corps. De toutes origines et de tous statuts confondus, les exilés développent une tendance à ressasser les images et les saveurs de leur vie passée, à se replonger par l’imagination dans le bain d’expériences sensorielles qui était le leur ou qu’ils imaginaient être le leur auparavant. Le rapport à la nourriture de la localité « perdue », dans le ferment sensoriel (goût, toucher, odorat, vue) qu’il contient, fait que les « aliments-mémoire » (Bahloul 1983) fonctionnent souvent comme des « objets-reliques » (Lussier 2011) que l’on tient à maintenir, conserver, et reproduire en exil.

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)0.002
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.007
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0190.002

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 head0.223
GPT teacher head0.517
Teacher spread0.294 · 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
Published2025
Admission routes1
Has abstractyes

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