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Record W3114992165 · doi:10.46472/cc.01216.0275

Presentation of Playing the Stars

2012· article· en· W3114992165 on OpenAlexaboutno aff
Toby MacLennan

Bibliographic record

VenueCulture and Cosmos · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsPlanetariumSculptureVisual artsArtQuiltArt historyHistoryAstronomyPhysics

Abstract

fetched live from OpenAlex

This talk reports on a performance of the planetarium event Singing the stars with sculptures, which was performed at the H.R. Macmillan Planetarium in Vancouver and the Seneca College Planetarium in Ontario. Subsequent performances were done at the Art Gallery of Ontario, P.S.1, New York City, the 10th International Sculpture Conference, Toronto, and The National Gallery of Canada. It was reviewed in the Village Voice, Arts Canada, The New Art Examiner, Vanguard Magazine, and CBC Radio: Out of the belly of Vancouver’s H.R. Macmillan Planetarium, the star-making machine rises to the star chamber, carrying three musicians with their instruments, and three sculptures. Atop each sculpture is an overhanging frame of five wooden bars, which acts as a musical score. Lights go down over the planetarium audience. Stars move across the sky. Only the constellations and the luminous bars atop the sculptures are visible. Swept up by the grandeur of the constellations, the musicians look up through the bars atop their sculptures and give a concert playing the stars. The concert is inspired by a story from my book, Singing the Stars. A village of people has lost the power of night, which once resided within them. The people attempt to lure the night back with the help of sculptures, which will enable them to play and sing the stars. They hope that, lured by the music, the night will come close to their faces, and bits of darkness will fall into their ears, eyes and mouths and gradually fill up their bodies with the night sky.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.135

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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 teacher head, 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
Published2012
Admission routes1
Has abstractyes

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