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Record W2905811411 · doi:10.7202/1054382ar

L’artiste contemporain dans les musées d’ethnographie ou la « promesse » d’un commissariat engagé

2018· article· en· W2905811411 on OpenAlexvenueno aff
Julie Bawin

Bibliographic record

VenueRACAR Revue d art canadienne · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionEthnographyIdentity (music)PoliticsSociologyArtColonialismPhenomenonHumanitiesArt historyMedia studiesVisual artsAnthropologyAestheticsHistoryPolitical scienceLawEpistemologyArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

For over three decades, ethnographic museums have been engaged in a process of redefining both their missions and their collections. Forced to reinvent themselves, as well as develop exhibition strategies in response to post-colonial theory, many of these museums have adopted a self-critical attitude and invited artists to intervene in their collections. What do such practices reveal ? When artists turn their attention to the collecting, archiving, and exhibition practices of colonial museums, does it follow that their approach is more engaged ? By considering the first exhibitions of this kind, while also tracing the evolution of this phenomenon since the 1980s, this paper seeks to respond to these questions. It also strives to understand the specific nature of critical curating as it is practiced in museums that are, more than any others, loci for identity politics.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0150.020
Scholarly communication0.0120.006
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.043
GPT teacher head0.220
Teacher spread0.178 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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