Offering the Model of Structural Equations for the Parameters of Leadership Thinking, Communication and Meritocracy in Training of the Organizational Leaders of Oil Company
Bibliographic record
Abstract
Organizations can be regarded as one of the main social foundations in the present age and management can be one of the most important factors of life, growth, and flourishing of the organizations. The issue of training of leaders has complicated and mixed dimensions and identity, therefore there is not a deep understanding of the concept among most of the administrators and also in the research – scientific centers level. This paper aims to offer a model of the structural equations of the consisting variables of the core concept (leadership thinking, communication, and meritocracy). In this research, the analytical – descriptive method was used to offer the model of structural equations for the parameters of leadership thinking, communication, and meritocracy in the training of the organizational leaders of Oil Company. The statistical population includes top, middle and first line managers of Oil Company and the questionnaires were prepared randomly by sampling and according to the Morgan table for 384 subjects. Quantitative analyze of data were performed by SPSS software version 19, and the structural equations were analyzed by the 8/7 LISREL software. Finally, the effect of each of the variables of leadership thinking, communication, and meritocracy on the main variable of core concept was determined by the offered structural model. The model reliability was confirmed by the goodness of fit indices.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".