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Record W2517288254 · doi:10.21226/t22p4c

Comparing the Soviet and Chinese Famines: Their Perpetrators, Actors, and Victims

2016· article· en· W2517288254 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEast/West Journal of Ukrainian Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSoviet and Russian History
Canadian institutionsnot available
Fundersnot available
KeywordsFamineIndustrialisationNationalityDevelopment economicsPolitical scienceCommunismChinaKazakhPolitical economyPoliticsEconomicsImmigrationLaw

Abstract

fetched live from OpenAlex

The Soviet (1931-33) and Chinese (1958-62) famines were man-made catastrophes that occurred in underdeveloped states with growing populations during peacetime and affected traditional surplus areas. Both are marked by overly ambitious industrialization strategies at the expense of the rural economy in which central authorities failed to lower grain quotas once famine broke out and even increased them. The famines also had differences, notably regarding the nationality or ethnic question, which played a key role in Ukraine and was present in the Kazakh famine, but was absent in the Chinese famine. Also, Chinese Communist Party leaders, notwithstanding the cruelty of their policies, were much better disposed towards peasants than were the Soviet Bolsheviks. One cannot ascribe murderous intent on Mao’s part, but rather an incoherency of policy and unwillingness to recognize and correct his errors.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.313
Teacher spread0.261 · 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