Bibliographic record
Abstract
Purpose Low‐income transitional countries in the region of the Caucasus and Central Asia lack the existence of a solid assessment of public perceptions regarding the causes of poverty during transition. The purpose of this paper is to fill that gap in the existing literature. Design/methodology/approach This paper uses the secondary analysis of a recent cross‐sectional multinational survey to shed light on public beliefs of the causes of poverty in seven countries of the region – Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan. In addition, Russia and Ukraine are used as a comparison point. The theoretical framework for this study is that the subjective beliefs regarding the explanations of poverty can be classified into three broad groups: individualistic, fatalistic, and structural. Hence, regression coefficients and marginal effects of the multinomial logit regression model (MNLM) are estimated to associate the set of various individual, households, and community characteristics selected in the conceptual framework with the likelihood of choosing one of the three afore‐mentioned explanations of poverty. Findings The results of cross‐tabulation reveal that in a majority of the countries studied, the predominant explanation for poverty is structural, with the exception of Tajikistan and Uzbekistan, where predominant explanations are, respectively, fatalistic and individualistic. The results of MNLM show that most individual, household, and community characteristics possess the expected direction and are in line with previous findings. However, some of the characteristics have a similar significant effect across several countries, while other characteristics are significant for a single country only. Social implications These findings demonstrate that despite the dominant post‐socialist ideology which favors individualistic and fatalistic explanations of poverty based on the economic rationality of market capitalism, the efforts of the elites in promoting and imposing these ideologies has not been fully successful. Nevertheless, no single unified model of the determinants of beliefs regarding the causes of poverty in the countries of the region is observed. Originality/value This is one of the very few papers aimed at assessing public perceptions regarding the causes of poverty in transitional countries of the Caucasus and Central Asia.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".