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Record W3132328172 · doi:10.1525/hsns.2021.51.1.87

Funny Origins of the Big Bang Theory

2021· article· en· W3132328172 on OpenAlexaff
Alexandre Bagdonas, Alexei Kojevnikov

Bibliographic record

VenueHistorical Studies in the Natural Sciences · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHistory and Developments in Astronomy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBig Bang (financial markets)CosmologyScientific theoryEpistemologyUltimate fate of the universeThermonuclear fusionUniverseState (computer science)SociologyPhilosophyPhysicsPhysical cosmologyAstronomyComputer science

Abstract

fetched live from OpenAlex

Popularization of science typically follows the lead of scientific research, conveying to lay audiences ideas and discoveries initially published in professional scientific literature and vetted by the expert community. The physicist George Gamow (1904–1968) did not respect this tradition, but promoted some of his most unorthodox scientific hypotheses as funny stories in his popular writings for non-specialists and teenagers, sometimes years before he dared to present them to the purview of academic peers in papers submitted to specialized research journals. Gamow’s proposal of the Big Bang cosmology—the theory that our universe started out in an explosive manner from a superhot and superdense state with thermonuclear reactions forming matter—was discussed by him initially in a series of non-serious articles and books, starting in 1938. Historians of cosmology recognize Gamow’s crucial contribution to the development of the Big Bang theory on the grounds of his subsequent professional publications but have not paid sufficient attention to his popular science writings and their role in changing our conception of the universe.

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)0.005
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.023
Scholarly communication0.0040.006
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.047
GPT teacher head0.303
Teacher spread0.256 · 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 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

Citations9
Published2021
Admission routes1
Has abstractyes

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