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Record W1965852825 · doi:10.5038/2162-4593.12.1.2

Landscape Aesthetics, Water, and Settler Colonialism in the Okanagan Valley of British Columbia

2008· article· en· W1965852825 on OpenAlexaffabout
John Wagner

Bibliographic record

VenueJournal of Ecological Anthropology · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of the Fraser ValleyUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsColonialismIndigenousSettlement (finance)TourismGeographyAgricultureCultural landscapeHistoryArchaeologyEcology

Abstract

fetched live from OpenAlex

Historic and contemporary patterns of settler colonialism and agricultural development in the Okanagan Valley of British Columbia are described, emphasizing the ways in which settler culture has led to the production of a landscape aesthetic that reproduces colonization as an iterative cultural practice. I explore the ways in which this particular landscape aesthetic is dependent on the economic and symbolic meanings of water in Okanagan settlement history. The images of the Okanagan that were used to attract settlers to the valley a century ago emphasized the lush, oasis-like qualities of orchards and lakes set among a dramatic, arid and mountainous backdrop. This oasis aesthetic exists in sharp contrast to that held by the Syilx indigenous people who were displaced and marginalized as a consequence of agricultural development. Today, as land prices escalate and orchards become less economically viable, it is the orchardists themselves who are being displaced by a new generation of settlers who come here to retire or make their livings in the wine tourism industry. As the environmental costs of these changes accumulate, Okanagan residents are challenged to articulate a more sustainable landscape aesthetic rooted in local ecology.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.999

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 studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.307
Teacher spread0.279 · 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.

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

Citations15
Published2008
Admission routes2
Has abstractyes

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