MétaCan
Menu
Back to cohort
Record W3140412610 · doi:10.3390/su13073747

Making the Invisible Visible: Eco-Art and Design against the Anthropocene

2021· article· en· W3140412610 on OpenAlexafffund
Carmela Cucuzzella

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAnthropoceneRealmEnvironmental ethicsRendering (computer graphics)Environmental resource managementAestheticsPolitical scienceComputer scienceLawArtEnvironmental scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This paper examines a series of art and design installations in the public realm that aim to raise awareness or activate change regarding pressing ecological issues. Such works tend to place environmental responsibility on the shoulders of the individual citizen, aiming to educate but also to implicate them in the age of the Anthropocene. How and what these works aim to accomplish, are key to a better understanding the means of knowledge transfer and potential agents of change in the Anthropocene. We study three cases in this paper. These are examined through: (1) their potential to raise awareness or activate behavior change; (2) how well they are capable of making the catastrophic situations, which are invisible to most people, visible; and (3) how well they enable systemic change in the catastrophic situations. In the three cases studied, we find that they are successful in helping to raise awareness and even change individual behavior, they are successful in rendering the invisible visible, but they are incapable of engendering any systemic change of the catastrophic situations depicted.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.290
Teacher spread0.273 · 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.

Study designObservational
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

Citations20
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueSustainabilitySame topicEnvironmental Education and SustainabilityFrench-language works237,207