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Record W3011386614 · doi:10.1162/octo_a_00377

Three Aral Sea Films and the Soviet Ecology

2020· article· en· W3011386614 on OpenAlexaff
Alec Brookes

Bibliographic record

VenueOctober · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSoviet and Russian History
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIndigenousMarxist philosophyEcologyAlienationScholarshipCognitive reframingDialecticSociologyEnvironmental ethicsHistoryPolitical scienceBiologyLawPhilosophyEpistemologyPolitics

Abstract

fetched live from OpenAlex

Drawing on analysis of three films that converge on the Aral Sea and span from 1929 to 1988, Alec Brookes engages with Marxist scholarship on the Anthropocene and Capitalocene to argue that the Soviet ecology rested on the same fundamental principle of the Capitalist world ecology: the alienation of indigenous producers from land in waves of primitive accumulation. The Forty-First (1956) and Turksib (1929) both show how, alongside other devices, the dialectics of film form as theorized by Sergei Eisenstein were repurposed to reframe the conquest of “Man” over “Nature” and ultimately to appropriate land from producers within an ostensibly Marxist framework. In The Needle (1988), on an already desiccated Aral Sea, director Rashid Nugamov suggests that the restoration of Asian land to Asian producers provides a way forward after the decay and depravation of the Soviet ecology. The analysis here suggests that to confront the Capitalist world ecology in the present we must work to restore land to indigenous producers and promote indigenous ecological relations.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0090.007
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.259
Teacher spread0.235 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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