MONITORING PARAMETERS RELATED TO INVESTMENT ACTIVITY IN THE AGRARIAN SECTOR OF REGIONS ECONOMIES
Bibliographic record
Abstract
The article aims at developing a methodological approach that allows obtaining relevant information about the potential investment recipient of the agro-industrial complex in terms of its territory and objecting. The urgency of this area of research is explained by the circumstance that unstable nature of production in agriculture and instability of indicators of its dynamics require identifying reasons of the instability of its indicators, as well as revealing the most important factors and conditions that restrict the tempos of economic growth in the agrarian sector of the Russian Federation. This situation had the greatest effect on the investment activity of sub-sectors of the agro-industrial complex. It does not allow getting on the path of technological modernization of the agrarian sector, and, as a consequence, it accentuates issues related to the food safety of the country. In that context the research improves methodological approaches that allow obtaining the relevant information about the potential investment recipient of the agro-industrial complex in terms of its territory and objecting. The offered approach is based on the diagnostics of the system of indicators developed by the authors for differentiating territories according to the level of their investment development. The authors formed the linguistic characteristics and economic interpretation of the singled classification groups, as well as the next monitoring stage that allows identifying a specific economic subject for investing funds on the basis of forming investment passports of potential recipients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".