Explanation of a Knowledge Workers Maintenance and Retention Model in Mapna Electric and Control, Engineering and Manufacturing Company (MECO)
Bibliographic record
Abstract
This research aimed to investigate and explain maintenance and retention model of Knowledge workers in MECO Company in 2015. The research statistical population consisted of all Knowledge workers with working conditions in MECO Company. Based on the estimation, the number of eligible employees was determined 138 people. 102 people among these were considered as sample size using Cochran formula and simple random sampling method. Data collection methods had been both qualitative and quantitative. Effective components were extracted on the basis of statements recorded from interviewees, discussions and dialogues, principles of observations, documentation and so on. It also was measured by researcher-made questionnaire. Questionnaire reliability was calculated using Cronbach's alpha method. Its value for all variables of questionnaire was acceptable values higher than 0.7. Similarly, the content validity was used to test the validity of the questionnaire. For this purpose the questionnaires were confirmed by relevant experts. Data obtained from the implementation of questionnaires were analyzed using SPSS software in two descriptive and inferential parts. Finally, the research results in the form of a model confirmed role of factors such as organizational structure of occupational satisfaction, communication system, evaluation and control system, organizational culture, pay and reward system, and job stress in the retention and maintenance of Knowledge workers in MECO Company.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".