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Record W4401056813 · doi:10.1016/j.enpol.2024.114266

Searching for stable electricity in Monrovia: Co-evolution of energy infrastructure and practices

2024· article· en· W4401056813 on OpenAlexaff
Phillip Garjay Innis, Kristof Van Assche, Detlef Müller‐Mahn

Bibliographic record

VenueEnergy Policy · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectricityEnvironmental economicsBusinessNatural resource economicsEngineeringEconomics

Abstract

fetched live from OpenAlex

Development requires action, adaptation, and transformation of both governance and physical infrastructure. Energy infrastructure unfolds as the infrastructure that facilitates the growth of other infrastructures and development. This paper argues it is critical to examine the complex, non-linear evolution of energy infrastructure and policy alongside the intricate, non-linear evolution of governance in general, and planning specifically, in southern cities, particularly those with a history of instability, scarcity and incomplete infrastructure. Monrovia, Liberia, provides a compelling example of the intricate co-evolution of policy, infrastructure and practices in unpredictable and unstable contexts that require adaptability, resilience and innovation. Understanding such a landscape is important because governance reflects characteristics of constant evolution, improvisation and searching within a broader system that is also evolving. The objective is to better grasp how the double and coupled processes of ‘searching’ and ‘planning’ interact to shape the landscape of options for tackling incomplete electricity infrastructure. The incompleteness of both governance and infrastructure retains benefits whilst the interplay of searching and planning can allow for positive adaptations in terms of governance and infrastructure; however, we equally know adaptation can take place in unsustainable contexts, thereby engendering the potential for risks and missed opportunities.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.300
Teacher spread0.288 · 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 designTheoretical or conceptual
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

Citations3
Published2024
Admission routes1
Has abstractyes

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