FORMATION OF THE HUMAN CAPITAL OF AGRICULTURAL COOPERATIVES: CANADIAN EXPERIENCE
Bibliographic record
Abstract
The article presents the results of theoretical and empirical research on what kind of institutional and technological changes modern cooperative organizations are experiencing and how the transformations taking place at the level of cooperatives affect the qualitative characteristics of their human capital. Firstly, the paper analyzes the most significant aspects that affect the human capital of agricultural cooperatives, such as the organizational model in which they operate, the principles on which their activities are based as a whole. At the same time, attention is drawn to the fact that the business orientation of modern cooperatives and its (to a certain extent) entrepreneurial (and not purely traditional) structure from the point of view of ownership, management, financing determine new requirements not only for the members of the cooperative (farmers), but also (perhaps to a greater extent) for managers, narrow specialists, employees. Secondly, the factors contributing to the intensification of competition in agricultural markets are considered, in particular, it is proved that new requirements for agricultural products from its consumers determine new strategies of cooperative behavior, and consequently, a new "personnel architecture" of cooperative organizations, in which such qualities of human capital as high qualification, professional flexibility, creativity, responsibility for results, leadership, and the ability to work as a team come to the fore. Thirdly, based on the generalization of the results of scrupulous research of domestic and Western scientists on human capital management, the practical experience of Canadian agricultural cooperatives (using the example of the modern Canadian cooperative "Agropur"), their problems are revealed (including those that are determined by modern challenges and threats associated with the coronavirus pandemic, unfavorable climatic conditions, political sanctions), the approaches to the successful development of agricultural cooperation through the formation of relevant human capital are determined.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".