The Siberian Curse: How Communist Planners Left Russia out in the Cold
Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».