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Record W2610731924 · doi:10.1386/vi.6.1.55_1

A curatorial perspective on MOA’s ćəsnaʔəm, the City Before the City

2017· article· en· W2610731924 on OpenAlexaffabout
Sandra Filippelli

Bibliographic record

VenueVisual Inquiry · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFraming (construction)WonderSituatedStorytellingVisual artsSociologyMedia studiesPerspective (graphical)Presentation (obstetrics)HistoryNarrativeArtArchaeologyPsychologyLiterature

Abstract

fetched live from OpenAlex

Abstract Museum of Anthropology (MOA)’s Fall 2015 exhibit, ćəsnaʔəm, the City Before the City, was an interactive artistic installation depicting contemporary and historical Musqueam First Nation life in the village of ćəsnaʔəm, situated at the mouth of the Fraser River in Vancouver adjacent to University of British Columbia. The co-curators worked with a Musqueam Advisory Committee of elders to create the kitchen table installation component entitled ‘gathered together’, consisting of a dining table and chairs inside a small room with an audio voice-over of Musqueam elders reminiscing about growing up to become the ‘knowledge keepers’ of their community. Curatorial practice and presentation drew museum visitors into an art education experience through memory, colloquial discourse, documentary storytelling, and visual art that particularly appealed as cultural history and socially engaged art in terms of intention, framing, making and wonder.

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)0.006
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: Other · Consensus signal: Other
Teacher disagreement score0.685
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0790.052
Scholarly communication0.0250.007
Open science0.0030.015
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0150.001

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 head0.147
GPT teacher head0.373
Teacher spread0.226 · 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
Published2017
Admission routes2
Has abstractyes

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