Formation of Emotional Intelligence of the Financial Company's Employees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it