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Record W3162531736 · doi:10.3390/su13105729

Glacier, Plaza, and Garden: Ecological Collaboration and Didacticism in Three Canadian Landscapes

2021· article· en· W3162531736 on OpenAlexafffundabout
Cynthia Imogen Hammond

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAnthropocentrismAgency (philosophy)SociologyEcologyHumanismEthosAestheticsEnvironmental ethicsArtSocial sciencePhilosophy

Abstract

fetched live from OpenAlex

The emphasis in landscape studies on human agency and needs can obscure the complex relationships between non-human living things and their animate and inanimate contexts. Diverse authors have pointed out that this anthropocentric outlook is problematic, destructive, and neo-colonial. How might it be possible to approach a landscape, i.e., land itself, and all that lives on it, in a way that foregrounds the realities and risks of that site, without falling back on familiar humanistic and anthropocentric tropes? In this essay, I explore three recent artworks that each engage with a different landscape: Requiem for a Glacier by artist and composer Paul Walde (2013); the Urban Prairie designed by landscape architects Claude Cormier + Associés (2012); and The Boreal Poetry Garden by visual artist Marlene Creates (born 2005-). By analyzing these artists’ and designers’ creative strategies in relation to these landscapes, I delve into the question of ecological collaboration in each project, and explore the ways in which the non-human aspects of the landscape do, or do not, take centre stage. In so doing, this essay has a second aim: to explore the extent to which, in performing a didactic relationship with their sites, these three projects contribute to an activist and pedagogical ethos around climate change, habitat, and ecology.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.228
Teacher spread0.221 · 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 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

Citations0
Published2021
Admission routes3
Has abstractyes

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