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Record W3197069131 · doi:10.32891/jps.v5i4.1311

Art and Environmental Action, One Bird at a Time

2020· article· en· W3197069131 on OpenAlexaffabout
Cameron Cartiere

Bibliographic record

VenueThe Journal of Public Space · 2020
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsEmily Carr University of Art and Design
Fundersnot available
KeywordsAction (physics)OddsOrnithologyGeographyChartEcologySociologyEnvironmental planningPublic relationsPolitical scienceBiology

Abstract

fetched live from OpenAlex

The environmental problems of climate change and species decline can feel overwhelming. Individuals are often at a loss, questioning what impact they can actually have. Through chART Projects, we have witnessed the dramatic effect of community-engaged art as a direct path to environmental action and impact on local ecosystems. During the 27thInternational Ornithological Congress, bird enthusiasts from around the world focused their attention on Vancouver, Canada. This article is a reflection on how chART took advantage of this assembly, creating an ambitious venture aiming for a sustainable effect on the public’s relationship to urban birds. As the Crow Flies was a public art project bringing creative connections to urban birds directly into the hands of the public. Works included sited-sculpture, community-engaged interventions, projections, workshops, performances, and 6,000 ceramic crows. chART’s founder, Cameron Cartiere has been working with an interdisciplinary team to address the loss of pollinators through Border Free Bees. That research project used environment-based art to engage communities to take positive action in order to improve conditions for pollinators, with tremendous success. As the Crow Flies took a similar approach to highlight the loss of bird species and actions individuals could take to improve the odds for their feathered neighbours.

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 categoriesInsufficient 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.068
GPT teacher head0.284
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
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
Published2020
Admission routes2
Has abstractyes

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