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Record W3207141117 · doi:10.1353/his.2021.0044

These Well-Wooded Towns: Supplying Fuel Wood to Central Canadian Urban Markets, 1867–1921

2021· article· en· W3207141117 on OpenAlexvenueaboutno aff
Joshua MacFadyen

Bibliographic record

VenueHistoire sociale · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsFirewoodConsumption (sociology)Agricultural economicsPopulationGeographyElectricityBusinessNatural resource economicsEngineeringEconomicsArchaeology

Abstract

fetched live from OpenAlex

In the late 1860s, Toronto subsidized two narrow gauge rail lines in an effort to meet rising energy demands, make use of its hinterland’s ability to supply firewood, and utilize railway charters to enforce cheap transportation of firewood from the hinterland to the city. Several of the new Dominion’s earliest railways were thus not the typical trunk lines for connecting distant population centres; rather, they were narrow gauge lines that ran into the forest for the purpose of supplying fuel to urban Canadians. Rising fuel prices, however, resulted in debates over what some considered “cordwood monopolies” and broken rail charters. An examination of the records of urban fuel merchants, statistics of locomotive fuel consumption, and Geographic Information System (GIS) maps of national railway freight reveal where firewood markets expanded and how urban firewood consumption intensified in southern Ontario cities. Wood remained an integral part of the modern urban energy system until at least the early 1920s, a period usually considered Canada’s age of coal, because firewood and coal had much in common as solid fuels and railway expansion created new markets for firewood from the Canadian Shield.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes2
Has abstractyes

Explore more

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