Sustainable Development of Agricultural Sector: Democratic Profile Impact Among Developing Countries
Bibliographic record
Abstract
The bullet point of Sustainable Development Goals (SDGs) 2030 is improving of countries food security through decreasing of hungry level and providing equal conditions for food to everyone. Besides, according to the findings the issues with hungry index could be solved through developing the agricultural sector corresponding of SDGs principals. The findings showed that the agricultural sector is start point of decreasing the hungry. The authors proved the type of the political regime had impact on the efficiency of achieving of SDGs and countries’ food security. The hypothesis of investigation was checking the relationship between political profile of the countries and level of sustainable development of the agricultural sector (ASI). The assessment of the relationship between average level of ASI and countries' democratic profile (democracy level of public relations) for 28 countries of Post-Soviet bloc proved the non-existence of differences between countries with authoritarian and transitional regimes opposed to other political regimes (imperfect and full democracy). The authors allocated three segments of countries: authoritarian and transitional regimes, imperfect democracy and full-fledged democracy. The findings proved the hypothesis that democracy level had a statistically significant impact on the average level of ASI. Using the bivariate and multivariate models the authors empirically proved that the democracy level increase by 1-point leads to the increase of the target index by 0.087 points for countries with authoritarian and transitional regimes (to which Ukraine belongs). Thus, the transition to a more democratic model of the political regime will partially offset the threats to food security.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".