Effect of Diversified Model of Organizational Politics on Diversified Emotional Intelligence
Bibliographic record
Abstract
Purpose behind the study is to explore the influence of organizational politics of bankers on the ability based emotional intelligence and their diversified interplay. The sample of 292 bankers was used for testing organizational politics’ effect on emotional intelligence as well as individual and collective effect of facets of organizational politics (general political behavior, going along to get ahead, and pay and promotions policies) on dimensions of emotional intelligence (self and others’ emotional appraisal, use and regulation of emotions). The results witnessed that organizational politics significantly affects emotional intelligence, whereas organizational politics’ dimensions significantly predict each emotional intelligence dimension collectively. It is also noted that general political behavior and going along to get ahead have a negative effect on the dimensions of emotional intelligence. But pay and promotion policies positively influences emotional intelligence dimensions. The study can be helpful for the managers, who can identify the patterns of political behaviors and the persons who use it by openly discussing it with the employees through training. Limitations and future suggestions are presented in later part.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".