Design, Explanation, and Evaluation of Training Model Structures Based on Learning Organization—In the Cement Industry with a Nominal Production Capacity of Ten Thousand Tons
Bibliographic record
Abstract
This research aims to determine the effectiveness of training based on learning organization in the staff of cement industry with production capacity over ten thousand tons. The purpose of this study is to propose a training model based on learning organization. For this purpose, the factors of organizational learning were introduced by qualitative research in the form of open codes, axial codes, selective codes and the resulted observations, and then the final model was obtained by structural equation model. The data were collected from the staff of three cement companies of Abyek, Tehran, and Sepahan, with a statistical population of 1719 staff of cement industry. The qualitative research sample included 29 experienced experts in the field of cement industry, and the quantitative research sample included 326 staff and experts, who were selected by multi-stage cluster sampling. A self-made questionnaire consisting of 72 questions was used to measure quantitative variables. The reliability of the questionnaire was 0.93 and its content and face validity was determined by expert colleagues and professors, the structural equation model and regression was used to analyze the quantitative data. The results showed that the status of learning organization in cement companies is in average level. Finally, the obtained model consisted of both individual and organizational factors. The individual factors affecting organizational learning include teaching scientific content, perception, trust, and self-efficacy of training. The organizational factors affecting organizational learning include organizational culture, forming the structure, the method of management and leadership, preparing human resource (identity), adaption to the environment, policies, rules, and regulations, and achieving a viable product. The share of individual factors on learning organization is higher than the effect organizational factors; the share of each factor is also determined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".