Self‐perceived social stratification in low‐income transitional countries
Bibliographic record
Abstract
Purpose Against a background of rising inequalities in transitional countries, the purpose of this study is to focus on the analysis of the self‐perceived social stratification in the low‐income countries of the South Caucasus. Design/methodology/approach Using data from the recent multi‐country comparative survey conducted in Armenia, Azerbaijan and Georgia, this study examines the factors explaining self‐perceived stratification in the region. Ordered logit regression model is fitted to assess the determinants of the stratification. Findings One of the most important findings of this paper is that the majority of the people in the examined region consider themselves as middle class, although a considerable share of the general population are actually at the lowest level of society. Self‐perceived social stratification in the countries of this region can largely be explained by a set of factors within the direct social policy domain. Practical implications Active promotion of job intensive economic growth, supporting small businesses, improving effectiveness of social protection policies, affordability of healthcare and education, and active integration of migrants and investment in public infrastructure should also be priorities. Social implications Addressing the identified policy priorities will permit counterbalancing stratification, supporting the middle class and reducing the poverty in the countries of the region. Originality/value To the best of the authors' knowledge, this is one of the first studies on the self‐perceived social stratification in the region of the low‐income countries of the South Caucasus.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".