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Record W2555087512

ART AS RESEARCH: UNTANGLING THE ECOLOGICAL CITIZEN

2010· article· en· W2555087512 on OpenAlexaboutno aff
Lisa K. Figge

Bibliographic record

VenueQSpace (Queen's University Library) · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsnot available
Fundersnot available
KeywordsCitizen scienceEcologyGeographyEnvironmental resource managementEnvironmental planningPolitical scienceEnvironmental scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

In this thesis, Lisa Figge analyzes the political space of ecological citizenship by theorizing her art practice.Beginning with an Arendtian lens, Figge creates projects in the vein of New Genre Public Art, to trace the qualitatively-distinct activities of the public sphere, in which ecological citizens appear.The art practices of Mierle Laderman Ukeles, Colette Urban, Pat Aylesworth, Helen and Newton Harrison help move this critique along.Then, taking Judith Butler's thinking on the bond between speech and action, or speech acts, Figge situates her art practice and thesis writing as an account of herself as an ecological citizen.Figge is interested in finding ways to multiply opportunities, for her and others, to perform concerned engagement with the world.In order to begin this process Figge acts out and analyses her three art interventions: Madame E and her suit of environmentally conscious a(r)mour, Ecological Citizen inTraining, and 86 Hands on Wolfe Island.In giving an account of herself, she shows how our aptitude for sorting things should not be used to override our capacity to make a meaningful life.The art exhibition Dust to Dust, 2010 is the twin of this accounting, which was held at Queen's University's Union Gallery.I would like to thank my advisors Ted Rettig and Mick Smith who supported my project to make art in Environmental Studies from my first contact with them.Ted who smoothed the way, ensuring I had everything I needed to make work in Ontario Hall, with his practical calm oversight of my sometimes overzealous ambitions, and Mick, for making me think harder and go more deeply into theories which, I had no idea were flowing just beyond my grasp.Thank you Katherine Romba, my third committee member, for teaching me about methodologies and pointing out the paradox

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.621
Threshold uncertainty score1.000

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 studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0350.002

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 head0.041
GPT teacher head0.257
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

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
Published2010
Admission routes1
Has abstractyes

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