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Record W2327701884 · doi:10.1090/noti862

Exhibit Review: Mathématiques, un dépaysement soudain (A Little More Mad Ophelia, Please)

2012· article· fr· W2327701884 on OpenAlexaff
Nathalie Sinclair

Bibliographic record

VenueNotices of the American Mathematical Society · 2012
Typearticle
Languagefr
FieldMathematics
TopicHistory and Theory of Mathematics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPhilosophy

Abstract

fetched live from OpenAlex

With the likes of David Lynch and Patti Smith as collaborators and the Fondation Cartier's elegant centre for contemporary art as the setting, the exhibition Mathématiques, un dépaysement soudain was irresistible, even given the six-hour train ride I'd have to endure to get from Torino to Paris.Before reaching the Fondation Cartier, I happened upon a much smaller, less ambitious show at the city hall of the fifth arrondissement, also devoted to the theme of mathematics meets art.It contained the usual array of images of fractals and circle packings, as well as architectural and geometric sculptures that seem to feature in public exhibitions devoted to convincing audiences that mathematics is much more like art than most people think.I knew the Fondation Cartier show would be different.It was.It was certainly grander and glitzier.It featured the crème de la crème of the European mathematics community, such as Misha Gromov and Sir Michael Atiyah.It had many more visitors than the humbler show in the fifth.It had a few breathtaking pieces, but it did not live up to its goals.These, according to the text written by one of the commissionaires of the exhibition, mathematician Jean-Pierre Bourguignon, were mainly didactic-to use the creativity of artists to help elucidate, for the public, the objects and practices

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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.158
Threshold uncertainty score0.528

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1580.115

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 head0.026
GPT teacher head0.303
Teacher spread0.277 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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Same venueNotices of the American Mathematical SocietySame topicHistory and Theory of MathematicsFrench-language works237,207