Land Reform in Russia: Institutional Design and Behavioural Responses
Bibliographic record
Abstract
Stephen K. Wegren. Land Reform in Russia: Institutional Design and Behavioural Responses. New Haven: Yale University Press, 2009. xix, 340 pp. Glossary. Appendices. Index. $55.00, paper.Stephen K. Wegren' s monograph provides a detailed analysis of the politics of land reform in post-communist Russia. The book will be of interest to specialists in Russian reform, to students of comparative agricultural policy and peasant studies, and to those interested in legislative processes in post-communist countries. Land Reform in Russia is clearly the fruit of countless years of careful research. The reader quickly forms the impression of an author who has seen more than a few seasons change in the Russian countryside. Surely there can be few greater challenges for a social scientist than studying land reform in a country with the largest amount of territory of any state in the world. One should acknowledge, with appreciation, that it is very labour-intensive to produce a monograph with this level of empirical detail.The monograph shows several particular strengths. Wegren provides an exhaustive account of the legislative evolution of Russian land reform, which at least until Putin's presidency proceeded in fits and starts until the final Land Code was adopted in 2002. Second, Wegren includes a detailed discussion of the legal regulation of land in Russia, painting a vivid picture of multiple layers of conditions imposed on the use of land. For the average rural resident, the end result could be described as a rather precarious set of rights. One must be careful when using the term privatization to describe Russia's land reform, because as Wegren points out, an individual's access to land may comprise some combination of family plots, shares of former collective farm land, rental arrangements, and rights of use. Privatization, therefore, is only one part of the land reform story.Third, the greatest strength of Wegren' s work is his discussion of survey research results and statistical data. In the second half of the book, the author documents the impact that reforms have had on Russian rural dwellers. The reader learns from Wegren that some citizens (predominantly male, well-educated, and well-connected) feel they have benefited from the process of change, but that many more peasants have experienced a sense of loss. Nevertheless, the odds of success are not completely stacked in favour of those who are already privileged. For instance, Wegren' s data suggests that bigger families working the land they occupy tend to accumulate more land than smaller families, because of the ability to concentrate the work of a larger number of people (pp. …
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".