Formation of Emotional Intelligence of the Financial Company's Employees
Bibliographic record
Abstract
Today human intelligence plays an important role in management activities. "Soft skills" are the basis for creating effective horizontal and vertical communications; however, for the effective management of employees today stands out another factor – management competencies, including emotional intelligence. Due to the ability to manage emotions, the employee is capable of self-motivation, to the effective management of conflict situations, work stress, and also increases the efficiency of staff. Accordingly, understanding the emotions of employees allows the financial company to analyze their actions and adjust them to create conditions that will satisfy the needs of the staff in exchange for meeting the needs of the organization if it is necessary. When considering the features of the formation of the emotional competence of employees, we found that emotional intelligence must be developed following the developed algorithm, especially leaders. The research also provides models for managing factors, as well as methods for assessing emotional competence and the mechanism for developing emotional intelligence on the example of retail trade (hypermarket with more than 300 employees) in Kazan.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".