Impact of Marital Status „Single“ on Behavior and Well-Being of an Employee or a Student
Bibliographic record
Abstract
The behavior of employees, their attitudes and interest in work are the subject of countless studies and investigations. Many look at the issue of employee motivation and stimulation from the point of view of position, education, age or gender. However, within the available findings, there is no knowledge regarding the status of employees, although the very fact of whether an employee lives alone or with someone else presupposes different behavior. The aim of this paper was to find out whether the status “single” has an impact on behavior, performance, and job satisfaction of an employee. As part of the search for information on this issue, we came across interesting findings of many authors who devoted themselves not only to this issue, but also investigated the issues of multicultural values, leadership, management in practice, education and other topics related to the influence of various factors on the performance and work of individuals. We tried to apply his survey, previously carried out in the USA and Canada to the territory of Europe/European Union, while taking into account multiculturalism and gender differences, since students participating in the Erasmus program took part in the survey too. We based our research on the fact that the shares and development of individual categories are the same in both regions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".