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Record W2054930192 · doi:10.1109/epec.2014.20

Assessment of Disruptive Innovation in Emerging Energy Technologies

2014· article· en· W2054930192 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsDisruptive innovationDisruptive technologyRenewable energyEmerging technologiesPhotovoltaic systemEnergy technologyComputer scienceRisk analysis (engineering)Technology developmentBusinessEnvironmental economicsMarketingEngineeringElectrical engineeringEconomicsManufacturing engineering

Abstract

fetched live from OpenAlex

The concept of "disruptive innovation" offers a basis for understanding the structure and dynamics of technology-driven markets, and how they respond to emerging technologies. The concept may also prove useful in assessing the relative prospects for research and development in related fields, such as nuclear, renewable or other energy technologies. A trial assessment was performed on the potential for innovations to disrupt energy markets. Consumer-level photovoltaic power in areas with high insolation was found to be the most potentially disruptive innovation in energy technology, possibly enabled by advances in electrochemical energy storage battery technology. Such findings could provide useful input to strategies for energy technology development. Quantitative measures of comparison are necessary, and gaps in technology coverage weaken the assessment. Formal methods to identify and assess innovations are being developed.

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.

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)0.003
metaresearch head score (Gemma)0.003
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.655
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.080
GPT teacher head0.405
Teacher spread0.325 · 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

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

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