Empirical analysis of job satisfaction determinants in Russia
Bibliographic record
Abstract
© 2015 Canadian Center of Science and Education. All rights reserved. Investigation into job satisfaction determinants is of high practical relevance since it acts as an indicator of particular groups of employees’ willingness to invest in development of professional competencies and skills. The paper presents results of empirical analysis of determinants of job satisfaction, satisfaction with professional advancement prospects as well as concerns over possible job loss carried out using panel data representing working population of Russia. The main results obtained lead us to the following conclusions. Employment functions as well as financial incentives have the strongest influence over job satisfaction and satisfaction with professional advancement prospects, while respondents representing all professions reviewed are generally equally concerned with possible job loss. Age and length of respondent’s employment are nonlinearly related to satisfaction indicators while gender differences are not significant. Residents of large cities are less frequently satisfied with their jobs and professional advancement prospects; however, they are much less concerned about possible job loss. Those employed by state-owned companies and agencies more frequently express satisfaction with their current positions. The analysis also reveals a distinct decline in job satisfaction level and in satisfaction with professional advancement opportunities in the crisis years of 2009 and 2010.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".