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Record W1530198111

The Siberian Curse: How Communist Planners Left Russia out in the Cold

2005· article· en· W1530198111 on OpenAlexaboutno aff
Victoria Levin

Bibliographic record

VenueDemokratizatsiya The Journal of Post-Soviet Democratization · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsnot available
Fundersnot available
KeywordsCommunismCursePer capitaCommunist stateInstitutionEconomic historyPolitical scienceLawDevelopment economicsSociologyPoliticsPopulationEconomicsDemography
DOInot available

Abstract

fetched live from OpenAlex

The Siberian Curse: How Communist Planners Left Russia Out in the Cold Fiona Hill and Clifford Gaddy. Washington, DC: Brookings Institution Press, 2003. 240 pp. $18.95 paperback.At a first glance, the title of The Siberian Curse: How Communist Planners Left Russia Out in the Cold by Fiona Hill and Clifford Gaddy, and the drab and desolate photo on the cover, bring forth two of the most stereotypical adjectives related to Russia: big and cold. Despite that initial impression, the authors' approach to Russia's geographical features, both in historical and economic terms, is original and well researched. Although recent literature on productivity and economic growth normally treats geography as the only exogenous variable, Hill and Gaddy argue that the allocation of human and physical capital across Russia was not an accident of nature's making. Indeed, Soviet central planners made policy choices that exacerbated the country's adverse geographical and climatic conditions.Hill and Gaddy draw on a variety of sources to support their thesis that productive resources were misallocated in Soviet Russia. To compare Russia's to other northern countries, the authors develop a statistic that should be useful for future research in this area. Temperature per capita (or TPC) is a populationweighted measure of mean January temperatures in different regions and in Russia as a whole. Since Canadian and Scandinavian populations are concentrated in regions with milder climates, Russia has the lowest TPC in the world. But is cold temperature really a curse? The authors contend that it is a major impediment to productivity, both of equipment and labor. In an attempt to quantify the cost of the cold, Hill and Gaddy refer to cold engineering research examining the effects of cold temperatures on workers' performance of different activities and provide bone-chilling accounts of machinery malfunctions as temperatures drop. Aside from production costs, people living in cold climes and the governments providing for them have to undertake adaptation costs in the form of heating, insulation of buildings, maintenance of infrastructure, and so on.After convincing the reader that the Russian winter is not merely a romantic concept glorified by the poets and feared by foreign armies, Siberian Curse traces the geographic history of the country to explain why millions of Russians ended up living in cities like Novosibirsk and Khabarovsk. Even before the Bolsheviks took power in 1917, Siberia was the destination for farmers looking for fertile soils and for prisoners banished from the European part of Russia. However, it was only during the Soviet period that a state-enforced, systematic, and perverse version of manifest destiny was implemented regarding Siberia. One interesting point made by the authors is that the forced-labor camp system (GULAG) was not the product of an overzealous ideological spirit, but a calculated solution to the shortage of voluntary labor faced by Communist central planners. To follow the writings of Friedrich Engels and spread production equally across the country's space, labor and capital had to be reallocated from the center to the regions, and forced labor was less expensive to move. According to Hall and Gaddy, the GULAG contributed the most to the spatial misallocation of resources within Russia. However, the ideology of developing Siberia was not abandoned with the demise of Stalin and forced labor camps. Various incentive schemes, financial as well as ideological, were adopted by Stalin's successors and lasted until the fall of the Soviet Union. …

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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.

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

Citations0
Published2005
Admission routes1
Has abstractyes

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