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The Geopolitics of Plant Pathology: Frederick Wellman, Coffee Leaf Rust, and Cold War Networks of Science

2020· article· en· W3080685475 on OpenAlexaff
Stuart McCook, Paul D. Peterson

Bibliographic record

VenueAnnual Review of Phytopathology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCold warGeopoliticsContext (archaeology)CONTESTLatin AmericansBiologySoviet unionEconomic historyRust (programming language)Political scienceEconomyHistoryLawPoliticsEconomics

Abstract

fetched live from OpenAlex

During the Cold War, coffee became a strategically important crop in the global contest between the United States and the Soviet Union. The economies of many US allies in Latin America depended upon coffee. In the Cold War context, then, the coffee leaf rust ( Hemileia vastatrix) became a geopolitical problem. Coffee experts in Latin America, which produced most of the world's coffee, began to prepare for an outbreak. In the 1950s, they built a global network of coffee experts. This network was sustained by US-led Cold War programs that promoted technical collaboration across the Global South, such as Harry Truman's Point Four programs. We explore the network's growth and evolution through one of its central figures, the American plant pathologist Frederick L. Wellman. This network has survived the end of the Cold War and evolved to reflect the new geopolitical context.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.010
Scholarly communication0.0060.010
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations3
Published2020
Admission routes1
Has abstractyes

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