Human Resource Management in Public Administration: Study on the Performance Measurement and Emotional Intelligence in the Workplace in Albanian Public Institutions
Bibliographic record
Abstract
This paper explores two important concepts to illustrate the potential of managers "emotional and performance measurement capital" within the organization, as well as how to provide them with the means to achieve what we call "emotional leverage".Who could teach you such things?You ccould only experiment on them.I truly believe that it is appropriate in this scientific paper to get back to the initial question arises often in our work relationships: Can Leadership be taught?I am pretty sure that I am not the only one who has such curiosity; I believe we all at some point in life have wondered if we can feel as a born Leader or become a Leader.Through years, researchers have been trying to find answers.Learning is the process of knowledge transfer from one person to another, and quite often while lecturing and sharing it in leadership courses.It is commonly said that at the end students have to find out for themselves whether or not they are Leaders!Leadership begins with self-confidence, courage and perspective.Later in this paper a discussion of a very important emotional variable takes place called emotional intelligence, or how to understand and manage our own and others' emotions.The goal of this paper is to describe the distinguishing skills of a leader, despite the thousands of traits that characterize each leadership style; as well as, bring into readers' focus the successful leadership experiences and profiles of well-known people.However, the main aim is to measure how important the Leader's emotional intelligence in Albania is.In addition, how to understand and manage our own and others' emotions, is broken down.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".