Features of training for the agricultural sector of the Sverdlovsk region
Bibliographic record
Abstract
Abstract. The purpose of the research is to identify the problems of providing qualified personnel for agricultural enterprises of the agro-industrial complex of the Sverdlovsk region and suggest ways to solve them. For this, it is necessary to conduct a serious and thorough study of it, through sociological, analytical and statistical methods. As the requirements for workers, specialists and managers increase, the need for improving the forms and methods of their training, creating an effective system of continuing professional education for all categories of workers increases. It is known that the source of replenishment of labor resources for agricultural enterprises (and other fields of activity) is young people and, in particular, graduates of higher and secondary educational institutions Results of the study: in order to conduct an objective assessment of providing agricultural enterprises in the Sverdlovsk region with young specialists, it is first necessary to determine the level of their interest in a future profession and the desire to work in the industry. Choosing a research method, a sociological survey we conducted it among students of the Ural State Agrarian University in the second quarter of 2019. Also, information was collected and analyzed on admission to places under the targeted admission quota in the areas of training and specialties at the Ural State Agrarian University for 2017–2019. Having studied the issue of career guidance for schoolchildren, it is necessary to strengthen the revival of agricultural classes and pay special attention to settlements in which there is a shortage of highly qualified personnel in agriculture. It is proposed to develop an online platform that will provide an opportunity to combine the needs of organizations in personnel and the issue of providing the student with a place of practice and further employment. The scientific novelty of the study consists in a set of measures, based on comprehensive monitoring of the state of personnel potential in the agricultural sector and includes not only socially significant areas, but also real mechanisms for their solution
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".