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Record W1949960135

MAKING NATURE’S VALUE VISIBLE: VIDEO ARTISTS AS CITIZEN SCIENTISTS

2014· article· en· W1949960135 on OpenAlexaffabout
Sarah Van Borek

Bibliographic record

VenueEuropean Scientific Journal ESJ · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicArtistic and Creative Research
Canadian institutionsEmily Carr University of Art and Design
Fundersnot available
KeywordsStorytellingGeneral partnershipStudioThe artsSociologyVisual artsValue (mathematics)Variety (cybernetics)Media studiesNarrativePolitical scienceArtLaw
DOInot available

Abstract

fetched live from OpenAlex

Rewilding vancouver community projects was a community mapping, storytelling & visioning project designed to reconnect residents in the Metro Vancouver region of British Columbia, Canada with the city’s “wild side” (the historical natural environment) and to help “make visible” the different forms that nature takes in Vancouver as ways to inspire the valuing, protection and potential “rewilding” of nature.. The project was developed through a two-semester cross-disciplinary studio-based community projects course in the Faculty of Culture + Community at one of Canada’s leading post-secondary art institutions, the Emily Carr University of Art + Design (Vancouver), in partnership with one of Canada's leading environmental organizations, the David Suzuki Foundation (DSF), and the bold, contemporary Museum of Vancouver (MOV). Postsecondary art students across a variety of levels and disciplines were facilitated in producing a media arts-based “virtual urban safari” as part of the MOV’s Rewilding Vancouver exhibit running February 27-September 1, 2014. This exhibit is believed to be the first of its kind in Canada to feature the historical ecology of a major city. Many of the students, by their own self-description, came into the course feeling disconnected from the natural world themselves. The course was structured in a way that encouraged students to reconnect with nature in the city through their creative process. The course culminated in a public screening and dialogue, offering the students the opportunity to see the effects of their work on a public audience and feel like their input could have an 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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.009
Scholarly communication0.0140.005
Open science0.0020.006
Research integrity0.0030.004
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 head0.048
GPT teacher head0.322
Teacher spread0.275 · 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 designQualitative
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
Published2014
Admission routes2
Has abstractyes

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