Data-Driven Management of Regional Food Security for Sustainable Development: A Case Study of Kazakhstan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of the paper is to evaluate the possibility of implementing a food security management system in the Republic of Kazakhstan using the concept of data-driven decisionmaking in terms of achieving the principles of sustainable development.To achieve the goal set in the study, the authors use qualitative and statistical methods for processing the results obtained.Based on the analysis of internal and external factors, the authors determine indicators of food security in Kazakhstan for basic types of agricultural products/food in 2021, factors of influence on regional food security, and indicators of regional food security, which should be considered when making data-driven management decisions.A special external factor for Kazakhstan is the current geopolitical situation caused by the invasion of Russian troops in Ukraine.The study finds that in the process of managing regional food security, the use of the data-driven decision-making concept makes it possible to adequately assess the initial state of the problem and determine the optimal methods for its solution.The study identifies key internal and external factors influencing food security in the region and proposes a data-driven decision-making algorithm for managing food security.The results highlight the potential of this approach for improving food security management in the context of sustainable development.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it