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Record W4401695532 · doi:10.1080/03057070.2024.2385879

A Climate History of Early Dutch Settlement at Cape Town, 1652–62

2024· article· en· W4401695532 on OpenAlexaff
Philip Gooding, Nadia Fekih

Bibliographic record

VenueJournal of Southern African Studies · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsMcGill University
Fundersnot available
KeywordsCapeSettlement (finance)ArchaeologyGeographyEconomics

Abstract

fetched live from OpenAlex

This article uses historical and climatological methods to recontextualise the first decade of Dutch settlement at the Cape, 1652–62. It draws on weather data contained in the Journal of Jan van Riebeeck, natural proxies in the region, climate reanalysis and the global context of a particularly severe period during the Little Ice Age to reconstruct climatic conditions at monthly and seasonal scales for the decade under review. This reconstruction shows that the Dutch settled at a time of declining moisture at the Cape: relatively wet summers gave way to dry winters, while short-term droughts and deluges were regular occurrences. It then integrates this climatic context into an understanding of the history of this period. In so doing, it argues that adverse climatic conditions helped to undermine early Dutch designs on intensive agriculture, accelerated many free burghers’ initial transition towards pastoralism, shaped the timing and volume of the cattle trade between settlers and indigenous populations and exacerbated tensions that led to the first Khoikhoi–Dutch war (1659–60). It may also have enhanced the transmission of and deaths from disease, though the evidence in this instance is less conclusive. In short, a climate-historical perspective shows that adverse climatic and environmental factors critically affected many of the core themes that are already associated with the history of early Dutch settlement at the Cape.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.038
GPT teacher head0.256
Teacher spread0.219 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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