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Record W3176667884 · doi:10.1163/1569206x-12341887

Energetika: Gleb Krzhizhanovskii’s Conception of the Nature–Society Metabolism

2021· article· en· W3176667884 on OpenAlexaff
Daniela Russ

Bibliographic record

VenueHistorical Materialism · 2021
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTechnocracyContext (archaeology)ElectrificationCommissionArgument (complex analysis)ScholarshipRelation (database)SociologyPolitical scienceNeoclassical economicsEpistemologySocial scienceEconomicsPoliticsLawPhilosophyElectricityHistoryChemistry

Abstract

fetched live from OpenAlex

Abstract In recent years, there has been a growing interest in the relation between Marxism and the Soviet productivist economy. While historical scholarship rarely explores the intellectual context in which the Soviet experiment unfolded, ecomarxists tend to describe the Soviet Union’s mistaken path as a result of the loss of ‘metabolic’ thinkers following the rise of Stalin. This article challenges the neat, purported divide between a ‘metabolic’ and ‘productivist’ Marxism by analysing the energy-economic thinking of Gleb M. Krzhizhanovskii, a Bolshevik engineer and old friend of Lenin. As chairman of both the electrification commission ( GOELRO ) and the State Planning Commission (Gosplan), Krzhizhanovskii conceptualised the energy economy as something embedded in the metabolism of nature and society and as the technical-economic basis of the socialist economy. This argument drew its strength from his idea that production is part of the general, ongoing life-process, and the hope that large-scale electrification and electro-chemistry could help govern the metabolism between nature and society more rationally – both arguments commonly found among contemporary natural scientists. Any ecomarxist attempt to recover the concept of metabolism today has to come to terms with its productivist and technocratic history.

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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.241
Teacher spread0.232 · 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 teacher head, 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

Citations2
Published2021
Admission routes1
Has abstractyes

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