MétaCan
Menu
Back to cohort
Record W3092712942 · doi:10.22215/etd/2020-13895

An Engineer’s Guide to Public Engagement in Renewable Energy Projects

2020· dissertation· en· W3092712942 on OpenAlexaffabout
Nathalin Moy

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsCarleton University
Fundersnot available
KeywordsRenewable energyPublic engagementContext (archaeology)Process (computing)EngineeringPublic participationWind powerPublic relationsEnvironmental economicsPolitical scienceEnvironmental resource managementBusinessArchitectural engineeringEnvironmental planningGeographyEconomicsComputer science

Abstract

fetched live from OpenAlex

This thesis examines the public engagement process for renewable energy projects, focusing on the extent to which public engagement factors into the technical design of the projects.This interdisciplinary research features a two-part literature review, summarizing the key academic literature on public engagement and engineering design, respectively.The resulting insights are distilled into eight guidelines directed to engineers looking or needing to incorporate public engagement into their design processes for renewable energy projects.A case study of the renewable energy landscape in Eastern Ontario under the Green Energy Act tests the validity and applicability of the guidelines.The analysis is based on consultation reports from eight solar and wind projects, supplemented by eight interviews with renewable energy stakeholders in the same geographical area.This research has implications for renewable energy projects, for policymakers, and for the engineering profession, particularly in the context of current initiatives to improve public engagement 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)0.006
metaresearch head score (Gemma)0.013
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: none
Teacher disagreement score0.042
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0030.003
Scholarly communication0.0040.006
Open science0.0020.004
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0420.029

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 head0.031
GPT teacher head0.329
Teacher spread0.297 · 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
Published2020
Admission routes2
Has abstractyes

Explore more

Same topicSocial Acceptance of Renewable EnergyFrench-language works237,207