A Study on Determining the Level of Individual, Procedural and Organizational Maturity Based on Integrated Pattern of People – Capability Maturity Model: (P-CMM) and 3- Dimensional Pattern of Organizational Maturity in Production and Industrial Organizati
Bibliographic record
Abstract
Excellence of the organization depends on directing improvements in all the fields and organizational dimensions. Excellence of the organization requires organizational maturity in three individual, procedural and organizational levels. The present research has been performed with the purpose of measuring the rate of organizational maturity in Mobarakeh Steel Company during 1389 in which from statistical population of managers and experts 105 out of 140 individuals have been selected based on Morgan Table. A questionnaire for determining the level of organizational maturity was designed for measuring the rate of organizational maturity and data were analyzed using SPSS and Minitab software and T, ANOVA Tests, post-test TUKEY and Pearson correlation coefficient. Studying reliability and internal stability of the test indicated that reliability for the questionnaire is 0.910 for measuring the rate of individual maturity, it is 0.952 for measuring the rate of procedural maturity, it is 0.934 for measuring the rate of organizational maturity, and in general, it is 0.962 for measuring total organizational maturity. The results show that the rate of individual maturity in Mobarakeh Steel Company is 81.18 %; it is 62.48% for procedural maturity, and 67.32% for organizational maturity.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".