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Record W4319601074 · doi:10.54097/ehss.v8i.4342

Policy Change in Renewable Energy Projects: A Case Study of the NECEC Project

2023· article· en· W4319601074 on OpenAlexaboutno aff
Yun Lang

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsHydroelectricityDilemmaEmbeddednessOpposition (politics)Climate changeGeneral partnershipCompetitor analysisPoliticsBusinessPublic administrationPolitical scienceEconomicsPublic relationsSociologyEngineeringMarketingLaw

Abstract

fetched live from OpenAlex

As countries face higher environmental and political pressure to combat climate change and accelerate the energy transition, hydroelectricity has secured its place as the prime candidate for a reliable and clean alternative to fossil fuels. However, the expansion of hydroelectric infrastructure has seen protests from local coalitions of preservationists and angry citizens, sometimes aided by the deep pocket of energy competitors, leading to the termination of several projects. This paper seeks to better understand this dilemma between ambitious climate goals and local opposition by analyzing the ongoing case of the NECEC, a hydroelectric transmission line proposed by a Hydro-Quebec-CMP partnership to deliver electricity from Canada to Massachusetts, US via Maine. The case study follows the theoretical foundation of the Advocacy Coalition Framework (ACF), a long-standing and empirically supported theory of public policy that emphasizes the role of coalitions in translating beliefs into policy change. In addition to confirming the framework's usefulness in explaining highly contentious cases, this study also provides critical insights into how coalitions spread favorable information, the strategic choice of political instruments, the partisan composition of coalitions, and the added complexity of involving a foreign company. To smooth out future large-scale projects, the paper makes recommendations for decision-makers based on bipartisan public engagement with community embeddedness, as well as better project design regarding fair compensation and reduced visibility.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.242
GPT teacher head0.443
Teacher spread0.201 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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