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Record W2995549542 · doi:10.1017/s0165115319000561

The Making of a Timber Colony: British North America, the Navy Board, and Global Resource Extraction in the Age of Napoleon

2019· article· en· W2995549542 on OpenAlexaboutno aff
Martin Crevier

Bibliographic record

VenueItinerario · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsNavyTonnageResource (disambiguation)ContingencyPort (circuit theory)BoomShipyardPoliticsState (computer science)HistoryEconomyGeographyEconomic historyArchaeologyPolitical scienceLawEngineeringAncient historyShipbuildingEconomics

Abstract

fetched live from OpenAlex

Abstract This article recounts the worldwide search for timber undertaken by the Navy Board, the administrative body under the authority of the Admiralty responsible for the supply of naval stores and the construction and repair of ships during the Napoleonic Wars. The closure of the Baltic by France and its European allies is considered the main factor in making British North America a timber colony. Yet the process through which the forests of the Laurentian Plateau and the North Appalachians came to fuel the dockyards of England and Scotland is taken for granted. To acquire this commodity, through merchants, diplomats, and commissioned agents, the power of the British state reached globally, reshaped ecological relationships, and integrated new landscapes to the Imperial economy. Many alternatives to the Baltic were indeed considered and tentatively exploited. Only a mixture of contingency, political factors, and environmental constraints forced the Board to contract in Lower Canada and New Brunswick rather than in areas such as the Western Cape, the Brazilian coast, or Bombay's hinterland.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.008
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.008
GPT teacher head0.244
Teacher spread0.235 · 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 designNot applicable
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

Citations9
Published2019
Admission routes1
Has abstractyes

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