The state and prospects of tourism industry in the conditions of the digital economy
Bibliographic record
Abstract
Given the lack of longitudinal data for transition countries, and specifically for Central Asia, research on poverty has largely ignored the time dimension. This study uses panel data constructed from the rotating cross-sectional Kazakhstan Household Budget Survey for the 2001-2009 period. The panel data provides an opportunity to measure chronic poverty levels and poverty transitions for the first time in Kazakhstan. We find that, despite the rapid and substantial reduction in poverty in the country since the turn of the century, and depending on the measure of chronic poverty employed, as much as a quarter of the population has experienced persistent poverty. However, the majority of chronically poor experience interrupted poverty spells. We apply the multiple-spell hazard model analysis to shed light on factors that impact on poverty exit and re-entry. The results of these estimates confirm that families with children under age six are experiencing higher probability of entry into poverty and lower probability of exit from poverty. Policy interventions are needed to improve the situation by providing an affordable state child care system in Kazakhstan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".