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Record W7077056280 · doi:10.64655/am.si21ca.iwthth1963

Affronts to Inertia: Atsa's Staged Encounters

2021· article· en· W7077056280 on OpenAlexaboutno aff

Bibliographic record

VenueArtMatters International Journal for Technical Art History · 2021
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownState (computer science)PoliticsPsychological interventionExpression (computer science)Power (physics)Public artPublic history

Abstract

fetched live from OpenAlex

In 1998, when the Montreal-based collective ATSA declared a state of emergency and held its first edition of État d’Urgence (State of Emergency), a socially engaged and temporary public art project seeking to call attention to the state of homelessness in downtown Montreal and of citizens around the world forced to exile because of wars or political instability, it did not anticipate that this project would unfold over the course of two decades. However, the recurrent need to remind the authorities and the public of this persistent issue, as well as the positive impact that the project had, led the artists to hold an edition of this project almost every year. Since Pierre Allard and Annie Roy founded ATSA in 1997, one of the main aims of their public interventions has been to stage encounters between complete strangers or between groups of people that do not necessarily talk to one another, such as homeless people and city representatives. By focusing on two of the collective’s long-term projects, namely State of Emergency (1998- 2017) and While Having Soup (2015–present), this paper examines the strategies employed by the artists to stage encounters and how they used the rallying potential of art to activate public spaces and to ensure that they remain open to free expression and democracy. This paper also discusses the material and immaterial traces left by these public interventions.

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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score0.582

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 science0.0010.000
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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueArtMatters International Journal for Technical Art HistorySame topicGeochemistry and Geologic MappingFrench-language works237,207