Social Mobility in the Post-Soviet Russia: A Revision of Existing Measurements byDrawing on Advanced Methods
Bibliographic record
Abstract
Most of existing studies of social mobility in the post-Soviet Russia provide measurements in terms of absolute mobility. However, the problem with such measurements is that they do not account for the structural differences when applied for cross-temporal (or cross-national) comparisons (which might be caused, for instance, by the change in the relative number of certain occupations, the expansion of higher education, etc.). Thus, absolute mobility (be it downward, upward or no mobility at all) does not distinguish the change which is caused by the institutional (i.e. qualitative), rather than the structural (i.e. quantitative) change.In this project we aim to fill in this gap and to analyze the dynamics of relative social mobility (i.e. mobility “net” of structural factors) in the post-Soviet period. Precisely, we would use the so called log-linear and log-multiplicative models designed to analyze contingency tables. By social mobility in this research we will understand intra- and intergenerational individual shifts in terms of occupational status, education and spatial (i.e. urban/rural) location.The data we will use come from representative surveys conducted in 1994, 2002, 2006 (2013 is work-in-progress at the stage of grant application). The surveys were initially designed by professor Shkaratan for the purpose of studying mobility and stratification processes in modern Russia. In order to guarantee the accuracy of our results we also intend to run parallel estimations by drawing on alternative data (RLMS, Generations & Gender Survey, International Social Survey Programme and European Social Survey).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".