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Record W2801647340 · doi:10.1111/cag.12464

Identifying ranching landscape values in the Calgary, Alberta region: Implications for land‐use planning

2018· article· en· W2801647340 on OpenAlexafffundvenueabout
Aimee Benoit, Tom Johnston, Ian MacLachlan, Doug Ramsey

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsBrandon UniversityUniversity of Lethbridge
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsStewardship (theology)GeographyLand useIncentiveEnvironmental planningPoliticsEnvironmental stewardshipEnvironmental resource managementCultural landscapeLandscape planningLand-use planningPolitical scienceEcologyEconomics

Abstract

fetched live from OpenAlex

In recent years, ranching landscapes in the region of Calgary, Alberta have experienced intensifying land‐use pressures related to urban growth and development. At the same time, Alberta's land‐use policies have introduced voluntary, market‐based incentives to encourage the conservation and stewardship of private land. Given this new emphasis, this study aims to better understand different perspectives of ranching landscapes among residents and landowners in two rural municipalities surrounding Calgary. Drawing on cultural landscapes and political ecology literatures, this paper identifies four broad categories of ranching landscape values that participants felt were important to maintain: lifestyle and community values; ecological values; production values; and economic values and property rights. Participants’ diverse place meanings suggest a need to expand the ways in which landscapes are understood and assessed within local contexts, as a strategy for promoting private land stewardship and for resolving tensions in land‐use planning processes.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.108
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.005
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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 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

Citations5
Published2018
Admission routes4
Has abstractyes

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