Data-Driven Management of Regional Food Security for Sustainable Development: A Case Study of Kazakhstan
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".