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Record W1954599363 · doi:10.7202/1030641ar

“It is the finest piece of government work that I know of anywhere”: The Influence of the Hydro-Electric Power Commission of Ontario on the Giant Power Survey of Pennsylvania, 1923-1927

2015· article· en· W1954599363 on OpenAlexaffvenueabout
Mark Sholdice

Bibliographic record

VenueScientia Canadensis Canadian Journal of the History of Science Technology and Medicine · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCommissionGovernorElectricityWork (physics)Government (linguistics)PoliticsElectric powerPower (physics)Public administrationEconomicsPolitical scienceLawEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Since its foundation in 1906, the Hydro-Electric Power Commission of Ontario exerted a major influence on the politics of electricity in the United States. American supporters of publicly-owned utilities saw the Hydro as a model worth emulating south of the border. Reformers who sought lower electric prices for consumers also looked to the Hydro for evidence of the technically-feasible lowest cost of producing and transmitting this source of energy. This paper will examine a specific instance when American Progressives sought to use the Hydro as both a source of information and inspiration for electric policy reforms: the Giant Power Survey of 1923-1927, an attempt by Pennsylvania Governor Gifford Pinchot to bring about lower electricity costs for consumers and to extend access to rural areas, through a mix of greater regulation and government action. The individuals involved in Giant Power came into close contact with Hydro officials for the vital administrative and technical information with which to argue for their cause; the Ontarians, however, had their own reasons to be wary of getting involved in a controversial proposal.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.051
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.013
GPT teacher head0.187
Teacher spread0.174 · 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 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

Citations1
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueScientia Canadensis Canadian Journal of the History of Science Technology and MedicineSame topicAmerican Environmental and Regional HistoryFrench-language works237,207