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Record W2234661914 · doi:10.1257/app.20140135

Understanding the Changing Structure of Scientific Inquiry

2015· article· en· W2234661914 on OpenAlexaff
Ajay Agrawal, Avi Goldfarb, Florenta Teodoridis

Bibliographic record

VenueAmerican Economic Journal Applied Economics · 2015
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFrontierIron CurtainInterpretation (philosophy)Political sciencePopulationEconomic geographyPositive economicsSociologyGeographyEconomicsDemographyCold warLawPhilosophyLinguistics

Abstract

fetched live from OpenAlex

The fall of the Iron Curtain led to an influx of new mathematical ideas into western science. We show that research teams grew disproportionately in size in subfields of mathematics in which the Soviets were strongest. This is consistent with the knowledge burden hypothesis that an outward shift in the knowledge frontier increases the returns to collaboration. We also report additional evidence consistent with this interpretation: (i) The effect is present in countries outside the United States and is not correlated with the local population of Soviet scholars, (ii) Researchers in Soviet-rich subfields disproportionately increased their level of specialization. (JEL I23, O31, O33, P36)

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)0.008
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.063
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.007
Science and technology studies0.0030.014
Scholarly communication0.0110.021
Open science0.0010.006
Research integrity0.0020.002
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 head0.650
GPT teacher head0.488
Teacher spread0.162 · 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 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

Citations58
Published2015
Admission routes1
Has abstractyes

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