Analysis of subjective wellbeing in low-income transitional countries: evidence from comparative national surveys in Armenia, Azerbaijan and Georgia
Bibliographic record
Abstract
Drawing on the comparative household surveys, this article examines subjective wellbeing in Armenia, Azerbaijan and Georgia, three low-income transitional countries located on the Caucasus. We found that economic factors explain a considerable part of the variation in subjective wellbeing. The results are significant and robust across all countries. Having a higher level of household income, university education and a larger number of people in household along with salary as a major income source positively affect subjective wellbeing. On the contrary, being unemployed or a migrant along with having social transfers as a major source of income negatively affect subjective wellbeing. Besides, subjective wellbeing is strongly associated with disagreement with the current direction of countries' development and withdrawal from discussing policy. We argue that analysis of subjective wellbeing can be used to enrich and validate the process of poverty analysis in the countries of the region.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".