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Record W7055191533

Context Matters: What Shapes Adaptation to Water Stress in the Okanagan?

2006· article· en· W7055191533 on OpenAlexaboutno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2006
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Adaptation (eye)Process (computing)EmbeddednessAgricultureClimate changeClimate change adaptationResource (disambiguation)
DOInot available

Abstract

fetched live from OpenAlex

This paper describes two case studies of demand-side water management in the Okanagan region of southern British Columbia, Canada. The case studies reveal important lessons about how local context shapes the process of adaptation; in these cases, adaptation to rising and changing water demand under a regime of increasingly limited supply in a semi-arid region. Both case studies represent examples of water meter implementation, specifically volume-based pricing in a residential area and as a compliance tool in a mainly farming district. While the initiative was successful in the residential setting, agricultural metering met with stiff resistance. These cases suggest many factors shape the character of the adaptation process, including: interpretation of the signal relative to context, newness of the approach, consumer values, and local and provincial political agendas. Although context has been explored in resource management circles, thus far climate change adaptation research has not adequately discussed the embeddedness of adaptation. In other words, how context matters and what aspects of context, unrelated to climate change, could encourage or thwart the act of adapting. This study is a simple illustration of the potential drivers, barriers and enabling factors that have influenced the adaptation process of water management decisions in the Okanagan.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.226
Teacher spread0.202 · 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 designNot applicable
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
Published2006
Admission routes1
Has abstractyes

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