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Record W2786206137 · doi:10.5539/jsd.v11n1p32

Defining Sustainability in the Greater Yellowstone Ecosystem

2018· article· en· W2786206137 on OpenAlexvenueno aff
Ryan D. Bergstrom

Bibliographic record

VenueJournal of Sustainable Development · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityAmenityNormativeGateway (web page)Environmental resource managementSustainability organizationsEcosystemPerceptionEcosystem servicesEnvironmental planningEnvironmental ethicsBusinessPolitical scienceGeographyEcologyPsychologyEconomicsComputer science

Abstract

fetched live from OpenAlex

Because of the normative and subjective nature of the terms sustainability and sustainable development, solutions tend to be applicable for specific regions but not the whole of society. Thus, it is imperative understand better how community stakeholders and decision makers define the concept of sustainability. Not only will greater understanding of such definitions add to our understanding of nature-society relations, but also in certain contexts, this understanding may help to promote realistic and effective decision-making at local levels. The objective of this study was to determine how amenity-driven gateway communities surrounding Yellowstone and Grand Teton National parks define, conceptualize, and perceive sustainability, and if those perceptions varied between time in residence, community of origin, or role within the community. Thirty-five key informant interviews were conducted with decision makers within the Greater Yellowstone Ecosystem to meet the study objectives. Throughout study communities, definitions of sustainability focused on the environment, the economy, and multi-generational thinking, and it is believed that these similarities can be the starting point for communication and collaboration among gateway communities, the long-term sustainability of their individual communities, and the collective resource upon which they all depend, the Greater Yellowstone Ecosystem.

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)0.004
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 score0.098
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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)0.0000.000

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 head0.035
GPT teacher head0.214
Teacher spread0.179 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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