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Record W1998141195 · doi:10.1162/jinh_a_00755

Testing the Limits of Climate History: The Quest for a Northeast Passage during the Little Ice Age, 1594–1597

2015· article· en· W1998141195 on OpenAlexaff
Dagomar Degroot

Bibliographic record

VenueThe Journal of Interdisciplinary History · 2015
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsSocial Sciences and Humanities Research CouncilWestern University
Fundersnot available
KeywordsClimate changeArcticCounterintuitiveThe arcticHistoryGeographyLittle ice agePhysical geographyEnvironmental ethicsClimatologyEcologyGlacierEpistemologyOceanography

Abstract

fetched live from OpenAlex

Although interdisciplinary scholars have firmly established the existence of an early modern Little Ice Age, methodologies that link climate, weather, and human history remain in their infancy. Journals kept during three Dutch expeditions to find a northeast passage through the Arctic between 1594 and 1597 demonstrate the complexity of establishing relationships between climate and human affairs. They confirm scientific reconstructions of the Little Ice Age in the Arctic, but they also record counterintuitive relationships between regional climate and local environments. These local manifestations of climate change shaped the course of the Dutch quest for a northeast passage in the 1590s, with important ramifications for Dutch economic and intellectual history. The journals reveal that historians must carefully establish distinct relationships between shifting environmental conditions and human activities across different scales before attempting to tie climate change to human history.

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.016
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.117
GPT teacher head0.372
Teacher spread0.255 · 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 designQualitative
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

Citations19
Published2015
Admission routes1
Has abstractyes

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