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Record W3121656589

The Journey Towards Decarbonization: Exploring Socio-Technical Transitions in the Electricity Sector in the Province of Ontario (1885–2013) and Potential Low-Carbon Pathways

2014· article· en· W3121656589 on OpenAlexaffabout
Daniel Rosenbloom, James Meadowcroft

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsElectrificationElectricityElectricity systemSociotechnical systemPoliticsPerspective (graphical)Momentum (technical analysis)Economic systemRural electrificationPolitical scienceEconomicsBusinessElectricity generationEngineeringPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

This article employs the multi-level perspective on socio-technical transitions to explore the historical evolution of the electricity regime in the province of Ontario from 1885-2013 and to interpret the potential for future movement towards decarbonization. With an emphasis on the political and social dimensions of transitions, this analysis traces the key features influencing change within Ontario's electricity system over the past century. This paper uses multiple criteria (the phase of electrification; role of the electricity system in economic development; structures of ownership, market and regulation; dominant technologies; and the relative stability of arrangements) to characterize distinct regime configurations and periods of instability which separate relatively stable system orientations. Lessons are drawn from the historical case with implications for future decarbonization in the province, including the importance of: (1) residual momentum; (2) embedded guiding principles; and, (3) politico-economic coalitions.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.710

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.006
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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