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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.010
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