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Record W4403761087 · doi:10.2218/forum.1.10039

Through Ecocriticism and Affect Theory, Exploring Climate Change Artistry: The Ice Receding/Books Reseeding Project

2024· article· en· W4403761087 on OpenAlexaff
Giada Ferrucci

Bibliographic record

VenueFORUM University of Edinburgh Postgraduate Journal of Culture & the Arts · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsWestern University
Fundersnot available
KeywordsEcocriticismAffect (linguistics)Climate changeAestheticsPsychologyArtEnvironmental ethicsPhilosophyGeologyCommunicationOceanography

Abstract

fetched live from OpenAlex

This article delves into the Ice Receding/Books Reseeding project of multidisciplinary artist Basia Irland through the lenses of ecocriticism and affect theory, uncovering its profound significance as a climate change artwork. An example of participatory artwork utilizing transmedia storytelling, this project provides a transformative platform that immerses audiences in the realities of non-human climate change impacts. Through an ecocritical examination, the article explores the intricate dynamics of human-non-human relationships depicted in the project, while affect theory sheds light on the emotional responses it evokes. Ultimately, I argue that participatory art is a crucial tool for climate change communication and activism, advocating for its broader adoption in addressing environmental challenges and fostering sustainability through the direct involvement of participants. Analyzing Irland’s Ice Receding/Books Reseeding project, the article underscores the potential of climate change art as a powerful medium for effective environmental communication and offers practical guidance for communicators aiming to optimize its impact.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.013
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.071
GPT teacher head0.252
Teacher spread0.181 · 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 designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFORUM University of Edinburgh Postgraduate Journal of Culture & the ArtsSame topicEcocriticism and Environmental LiteratureFrench-language works237,207