Investigation of the Relationship between the Organizational Cynicism Levels and Intent of Revenge of Sports Science Faculty Students Who Work
Bibliographic record
Abstract
The research aims to determine the level of relationship between the organizational cynicism and the tendency ofrevenge of sports science students. In this study, descriptive and relational screening technique was used as theresearch technique. The working group consists of 105 students (36 female, 69 male). The participants are studentwho study at Istanbul University Faculty of Sports Science in the academic year of 2017-2018. All of the studentshave been working at an organization/ a workplace for at least 6 months. As the data collection tool, two scales wereutilized with the personal information form prepared by the researchers. Organizational Cynicism Scale and RevengeScale were used. The percentage (%), frequency, skewness, kurtosis, t-test, ANOVA, Pearson Correlation analysiswere used. As a result there is a positive relationship between the level of organizational cynicism and intention ofrevenge. It was revealed that gender and type of employment variables did not make a significant difference inorganizational cynicism and sub-dimensions and will for revenge. However, significant differences have beenidentified in favor of the participants with a low salary in the behavioral sub-dimension of organizational cynicism.On the other hand, the scores of organizational cynicism and will for revenge of the participants are moderate andeven low.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".