Organizational Political Tactics in Universities
Bibliographic record
Abstract
The present research aimed to promote understanding of political tactics in organizations. Political behavior innowadays-complex conditions is a process that the conflicts, contrasts and differences among interested groups areresolved. It means dialogue, attention to different goals in organizations, regarding the interest of different groups,attraction of staff cooperation, and acquisition of the worker’s support in management decisions, therefore technicaland organizational wisdom are not sufficient. Managers along the development of organizations need to havepolitical wisdom. In this study we surveyed political tactics perceptions of 1263 academic faculty members in WestAzarbaijan State Universities. The research method was a descriptive-survey. Among these academic members, 376individuals were chosen randomly as research sample. Questionnaire of ‘political tactics’ (r= 0.9) was used to collectdata. The data were analyzed by using descriptive and inferential statistics as well as t-test, MANOVA, andFreidman test. Research findings showed that there were significant differences between academic degrees of theacademic faculty members and political tactics used in the universities, whereas there was no any differencebetween gender of faculty members and political tactics. The survey revealed that the perceptions of political tacticsamong faculty members were different in West Azarbaijan State Universities; therefore, some practical suggestionsare recommended.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".