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Record W4244438538 · doi:10.32920/ryerson.14653617.v1

A comparative policy analysis of wind farm development strategies in Ontario and Germany

2021· preprint· en· W4244438538 on OpenAlexaboutno aff
Leo Lau

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)LegislationWind powerIncentivePublic participationRenewable energyEnvironmental planningPublic opinionSocial acceptanceBusinessEnvironmental resource managementEconomic growthPolitical sciencePublic administrationEngineeringEconomicsGeographyPoliticsLawMarket economy

Abstract

fetched live from OpenAlex

Wind farm development strategies are compared with respect to gaining public acceptance between Ontario and Germany. Public opposition to wind farm development is currently experienced in Ontario and strategies employed by Germany to mitigate public opposition and gain public acceptance have been proven to be effective. These two jurisdictions are comparable due to similar jurisdictional responsibility for renewable energy development and implementation of climate change goals. Historical factors have been shown to trigger wind farm development for Ontario and Germany but certain historical events have played a larger impact to public acceptance in Germany. Germany has been able to maintain and increase the level of public acceptance to wind farm development by utilizing inclusive planning legislation and encouraging community based incentives. Ontario has developed legislation to increase wind farm development but has increased wind farm opposition. Ontario could increase public acceptance to wind farm development by learning from Germany’s experiences.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.048
GPT teacher head0.348
Teacher spread0.300 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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