MétaCan
Menu
Back to cohort
Record W7027246845

Carey Young: Subject to Contract

2013· book· en· W7027246845 on OpenAlexaboutno aff

Bibliographic record

VenueUCL Discovery (University College London) · 2013
Typebook
Languageen
FieldEnvironmental Science
TopicEducation, Technology, and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodHyporeflexiaTSG101DysgeusiaPretext
DOInot available

Abstract

fetched live from OpenAlex

Since the late 1990s, Carey Young has investigated the growing influence of international corporations on the individual in works that span a variety of media including video, performance, text, and installation, and which draw on the tradition of Conceptual art. Notably, she studies how language is transformed by corporate culture, or how contractual structures and their linguistic markers progressively pervade and reshape all domains of life. Like a double agent, she immerses herself in the business or legal worlds, donning the appropriate attire and enacting recommended scenarios in order to examine and question the reach of each institution’s power, and its ability to shape our contemporary reality. The publication was published in conjunction with Carey Young’s first solo exhibition in Switzerland, curated by Raphael Gygax, and offers an overview on her works from 2003 to today. It includes contributions by the artist, Martha Buskirk, Raphael Gygax, and Tirdad Zolghadr. Carey Young has presented her work in numerous solo exhibitions, including at the Paula Cooper Gallery, New York (2010); the Contemporary Art Museum, St. Louis; and The Power Plant, Toronto (both in 2009); she participated in the Taipei Biennial in 2010, the Moscow Biennial in 2007, the Sharjah Biennial in 2005, and the Venice Biennial in 2003.

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)0.004
metaresearch head score (Gemma)0.011
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 score0.032
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.021
Scholarly communication0.0110.008
Open science0.0010.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0260.004

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 head0.010
GPT teacher head0.205
Teacher spread0.196 · 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueUCL Discovery (University College London)Same topicEducation, Technology, and EthicsFrench-language works237,207