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Record W1981925107 · doi:10.3934/energy.2014.4.424

Wind energy development and perceived real estate values in Ontario, Canada

2014· article· en· W1981925107 on OpenAlexaffabout
Chad Walker, Jamie Baxter, Sarah A. Mason, Isaac Luginaah, Danielle Ouellette

Bibliographic record

VenueAIMS energy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsWestern University
Fundersnot available
KeywordsReal estateProperty valueWind powerProperty (philosophy)Value (mathematics)EstateBusinessSociologyMarketingEngineeringMathematicsStatisticsFinance

Abstract

fetched live from OpenAlex

This paper focuses on public concerns about real estate value loss in communities in the vicinity of wind turbines. There are some conflicting results in recent academic and non-academic literatures on the issue of property values in general—yet little has been studied about how residents near turbines view the value of their own properties. Using both face-to-face interviews (n = 26) and community survey results (n = 152) from two adjacent communities, this exploratory mixed-method study contextualizes perceived property value loss. Interview results suggest a potential connection between perceived property value loss and actual property value loss, whereby assumed property degradation from turbines seem to lower both asking and selling prices. This idea is reinforced by regression results which suggest that felt property value loss is predicted by health concerns, visual annoyances and community-based variables. Overall, the findings point to the need for greater attention to micro-level local, and interconnected impacts of wind energy development.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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

Citations20
Published2014
Admission routes2
Has abstractyes

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