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Record W2993507488 · doi:10.1093/jahist/jaz528

Sea Otters: A History

2019· article· en· W2993507488 on OpenAlexaffabout
Susan Nance

Bibliographic record

VenueJournal of American History · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOtterGeographyEndangered speciesPopulationHistoryEcologySociologyBiology

Abstract

fetched live from OpenAlex

Richard Ravalli's Sea Otters tells the story of Enhydra lutris in the northern Pacific Ocean over three centuries. This concise volume deftly tackles the relevant histories of Russia, China, Japan, the United States, Canada, and Mexico. The author draws upon no manuscript collections but instead a broad range of materials, from conservation and ecology science to narratives of trade and exploration to government materials and newspapers to recent popular primary sources. As a work of quasi synthesis, this book helpfully lays a foundation that will certainly support later archival work on sea otters and related species who inhabited the same Pacific Rim ecosystems. Along with the author's attention to many new stories from the Pacific world, the volume reveals some important historical lessons: that imperial expansion and regional otter extinction were directly connected as diplomacy and global trade intersected with the day-by-day decisions of hunters; that “the sea otter trade—for better or worse—revolutionized Pacific communities by introducing them to global capitalist systems from which they had been disconnected prior to the late eighteenth century” (p. 61); the cruel historical irony that, due to their near destruction of the species in many places, nineteenth-century hunters were the only ones who knew enough about otter behavior to inform early conservation policies of that era; and, a truism of animal history in general, that one species can receive multiple human interpretations over time and space—in sea otters' case, as predator, prey, endangered population, internet cutie, and aquarium marketing mascot.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.255
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 teacher head, 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

Citations0
Published2019
Admission routes2
Has abstractyes

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