The Role of Active Social Loafing and Psychological Encouragement in Human Capital Development
Bibliographic record
Abstract
Research aims: The general purpose of this study is to look at previous research on individual performance and several factors that can effect it, such as psychological encouragement and social loafing.Design/Methodology/Approach: Surveys were used to collect data for this type of quantitative study. The census method was employed in this study, with 124 respondents from the Instructors Department of Central Java National Police School representing 100% of the population. Simple regression analysis was utilized to determine the influence of independent and dependent variables.Research findings: Active social loafing negatively and significantly influenced instructor’s individual performance at a National Police School, while psychological encouragement positively and significantly influenced the instructors’ individual performance. In addition, psychological encouragement moderated the influence of social loafing on the Instructor's Department of the Central Java National Police School, representing individual performance.Theoretical Contribution/Originality: This literature can be used as a recommendation and additional information concerning management practices at the Central Java National Police School, in which social loafing has mainly occured.Practitioners/Policy Implications: This observation’s data and findings can be a reference point for for future social, psychological, and individual development research.Research Limitations/Implications: Demographic factors might affect an individual's performance and were not used in this research as control variables.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".