MétaCan
Menu
Back to cohort
Record W3014941101 · doi:10.1017/s0007087420000059

Programming the USSR: Leonid V. Kantorovich in context

2020· article· en· W3014941101 on OpenAlexafffund
Ivan Boldyrev, Till Düppe

Bibliographic record

VenueThe British Journal for the History of Science · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsUniversité du Québec à Montréal
FundersBundesministerium für Bildung und ForschungUniversité du Québec à Montréal
KeywordsIdeologyProsperityTechnocracyPoliticsScholarshipSoviet unionContext (archaeology)State (computer science)SociologyPolitical economySocial sciencePolitical scienceLawHistoryArchaeologyMathematics

Abstract

fetched live from OpenAlex

In the wake of Stalin's death, many Soviet scientists saw the opportunity to promote their methods as tools for the engineering of economic prosperity in the socialist state. The mathematician Leonid Kantorovich (1912-1986) was a key activist in academic politics that led to the increasing acceptance of what emerged as a new scientific persona in the Soviet Union. Rather than thinking of his work in terms of success or failure, we propose to see his career as exemplifying a distinct form of scholarship, as a partisan technocrat, characteristic of the Soviet system of knowledge production. Confronting the class of orthodox economists, many factors were at work, including Kantorovich's cautious character and his allies in the Academy of Sciences. Drawing on archival and oral sources, we demonstrate how Kantorovich, throughout his career, negotiated the relations between mathematics and economics, reinterpreted political and ideological frames, and reshaped the balance of power in the Soviet academic landscape.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0030.004
Open science0.0000.002
Research integrity0.0010.004
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 head0.061
GPT teacher head0.229
Teacher spread0.168 · 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 designQualitative
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

Citations30
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueThe British Journal for the History of ScienceSame topicEconomic Theory and InstitutionsFrench-language works237,207