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Record W4377260948 · doi:10.1515/9780773572805-003

Preface

2005· book-chapter· en· W4377260948 on OpenAlexfundaboutno aff

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2005
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
FundersNational Museum of Natural HistorySmithsonian InstitutionGoddard Space Flight CenterMemorial University of NewfoundlandEli Lilly and CompanyMuseum of Comparative Zoology, Harvard UniversityHarvard University
KeywordsPhilosophy

Abstract

fetched live from OpenAlex

The economy of Newfoundland and Labrador has historically depended on the exploitation of natural resources, especially cod and seals.The operations and socioeconomic impacts of these fisheries are well understood.What has not been well documented is the local "modern" shore-station whaling industry in the years between 1898 and 1972.Although whaling did not have the same overall economic impact as cod fishing and sealing, it provided important employment for residents of communities adjacent to the stations.The narrative that follows examines the conduct and impact of this small but important phase of marine resources utilization in Newfoundland and Labrador, and of global whaling.Commercial whaling was often highly cyclical, with each cycle proceeding through stock discovery, exploitation, expansion of the industry and increased competition, and resulting stock depletion.The introduction of more sophisticated technologies and techniques reduced stocks further.Whaling became unprofitable, and operations closed.The modern whaling industry of Newfoundland and Labrador followed the pattern.This study examines the seasonality of operations, changing spatial relationships, and environmental and biological factors that influenced the origin, rise, and decline of each contiguous cycle of the local industry.Information was collected from archives in Newfoundland, Canada, the United States, Scotland, England, and Norway.The principal primary sources included government papers, reports, legislation, and statistical summaries, whaling company documents and data, public and private petitions, oil and whalebone manufacturer lists, pamphlets, diaries, letters, and telegrams.Information was usually sporadic, incomplete, and in the case of catch data, variable in different sources.Newspapers were primarily used to develop the framework of the study and the industry profile.They provided statistics on oil and bone

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Other
Teacher disagreement score0.467
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.5330.286

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 head0.015
GPT teacher head0.202
Teacher spread0.186 · 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.

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

Citations0
Published2005
Admission routes2
Has abstractyes

Explore more

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