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Record W3016635122 · doi:10.1142/9781786348609_0006

Improving Renewable Energy Governance: Insights from Low-Carbon Investment Community Stakeholders

2020· book-chapter· en· W3016635122 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

VenueWORLD SCIENTIFIC (EUROPE) eBooks · 2020
Typebook-chapter
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRenewable energyCorporate governanceBusinessInvestment (military)Natural resource economicsEnvironmental economicsCarbon fibersEconomicsFinancePolitical scienceMaterials sciencePoliticsEngineering

Abstract

fetched live from OpenAlex

Social and economic infrastructure are integral to maintaining a society’s quality of life. Infrastructure remains one of the few topics for which unanimous political support seems plausible, and every year, approximately $2.7 trillion are spent worldwide on infrastructure projects such as ports and bridges (Authers, 2015). Yet this seemingly enormous sum masks an infrastructure expenditure gap (i.e., the difference between what is spent and what should be spent) of at least $1 trillion per year — a number which may grow in the coming years. This is due to ongoing contributors such as exploding population and economic growth in emerging markets, historical underinvestment in infrastructure, and misalignment of the interests and needs of the private sector, the public sector, and civil society in infrastructure-related decisions (Authers, 2015)…

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.907
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.002
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 head0.043
GPT teacher head0.209
Teacher spread0.166 · 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