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Record W3146913505 · doi:10.1353/rss.2016.0013

Russell the Rainmaker: Touring in Early Cold War Australia

2016· article· en· W3146913505 on OpenAlexvenueno aff
Jo Grant

Bibliographic record

VenueRussell the Journal of Bertrand Russell Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityHappinessCivilizationCold warOptimismHistoryEnvironmental ethicsEconomic historyPolitical scienceLawArchaeologyPhilosophyEpistemology

Abstract

fetched live from OpenAlex

During his 1950 lecture tour of Australia, Bertrand Russell was given the nickname “Russell the Rainmaker” due to unseasonably wet weather in the eastern states that appeared to accompany his travels. This humorous name represents a central idea that shaped his tour. Russell was convinced that the technical ability to make rain could transform Australia’s largely dry landscape and boost the nation’s farming potential, which could, in turn, support a new age of happiness and prosperity. Russell used this vision of the future to imagine a safe refuge for Western civilization in case Europe was destroyed in a possible nuclear Third World War. This paper will discuss how his Australian tour was an important moment of both hope and anxiety in his life. Russell’s idea of a pastoral utopia in Australia that relied on optimism about the capability of science to transform the landscape can be understood as a key way in which he responded intellectually to the events of the early Cold War.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0150.006
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.059
GPT teacher head0.320
Teacher spread0.261 · 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 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
Published2016
Admission routes1
Has abstractyes

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