MétaCan
Menu
Back to cohort
Record W3162404201 · doi:10.21606/drs.2014.67

Ecotone: Finding Common Ground Across Art, Science and Ranching

2014· article· en· W3162404201 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of DRS · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsEcotoneCommon groundComputer scienceEnvironmental scienceRemote sensingGeologyEcologyPsychology

Abstract

fetched live from OpenAlex

This paper uses the case study of Ecotone, a project that sought to bring disparate groups of people (artists, scientists, ranchers) together for shared discourse and potential action around agricultural environmental stress in southern Alberta, Canada. We explore this project from the perspective of an artist and designer. We examine a framework that values space, time and the pairing of people from different disciplines to encourage meaningful collaboration and interaction. Environmentalism and climate change are divisive topics, particularly in Alberta where the controversial oil and gas industry has made it Canada’s wealthiest province, resulting in both environmental indifference as well as extensive protests locally and from abroad. It is well acknowledged there is a need for better communication about the environment for real progress in protecting our resources to begin. Ecotone begins this conversation by inviting artists and designers to respond to the science and pragmatic realities of land stewardship.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.255
Teacher spread0.242 · 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