A Planning Model for Improving Personnel Competence in Pursuit of Sustainable Development
Bibliographic record
Abstract
The primary objective of this article is to explore ways to improve personnel competence in the context of sustainable development. To achieve this, our key scientific task is to develop a planning model for enhancing personnel competence within the context of sustainable development for a selected organization. The organization's personnel is the subject of this study. Our research was motivated by the objective of discovering ways to enhance personnel competence for socio-economic systems like an organization, all within the context of sustainable development. As a result, we have developed a contemporary three-level planning model for improving personnel competence in the context of sustainable development. Each level of the model is presented in detail and characterized accordingly. We used the IDEF technique as our primary modeling method, and the planning model was developed using vector programs. The key elements of the planning model include graphic visualization, context characterization, and information accessibility. The novelty of our research results lies in the formation of a modern methodological perspective on increasing personnel competence in the context of sustainable development. However, this article has a limitation: the study was conducted solely in the context of improving the planning process. Consequently, we only characterized one stage-planning. Our future research will be directed toward studying all stages of improving personnel competence in the context of sustainable development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".