Development of population farms abroad: features of name and support measures
Bibliographic record
Abstract
In this article, the formulated reference names of alternative variants of farms of the population abroad, which are small objects of management in the agricultural sector, are widely studied. Alternative versions are used to define and describe the features and similarities of the population with the economic concept. It is explained that the variety of definitions of names is related to local national characteristics. In addition, important information from a number of foreign scientists is presented, describing small forms of economic management of the population, various reflections, explaining reference names. A number of reference names are associated with local national characteristics. It is noted that in the Western literature, the concept of personal subsidiary farming coincides with the concepts of family farming or semi-farm farming, while the term does not coincide with one form of farming in Kazakhstan, but corresponds to two types, i.e., the objects of farming and the economy of the public, as well as their various infrastructure and statistics. As an example, the equivalents of farms in the United States and Canada were studied, the state of their development, the existing support measures in these countries were considered, and generalizing features were found on the basis of which the scheme of support measures was developed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".