Modeling the Effectiveness of Employee Compensation Based on Financial Resources
Bibliographic record
Abstract
Modeling the effectiveness of employee compensation by evaluating the relationship with the factors of the labor intensity of products, work experience, and incentive payments based on a linear model of multiple regression on the main components. In this paper, several methods are utilized, including the classical least squares method, variation inflation factor, principal component method. It is expected with theoretical representations that the labor intensity of products reduces the efficiency of employee remuneration, the experience and incentive payments in the General Fund of remuneration positively contribute to the increase in the efficiency of employee remuneration. The expediency of applying linear regression to the main components for measuring internal corporate factors of the employee remuneration system is shown since the linear model of multiple regression can give incorrect estimates due to collinear regressors. A methodological way to modeling employee remuneration effectiveness based on a regression on individual determinants of the motivation and remuneration system has been developed. The developed methodological means to modeling employee remuneration effectiveness has been tested on a poultry enterprise's data for the period from January 2015 to March 2020. The article's main conclusions can be used in the scientific and practical activities of agricultural enterprises in measuring and evaluating the effectiveness of using financial resources to pay.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".