Identifying the management barriers for establishing suggestions system: A case study of educational system
Bibliographic record
Abstract
Suggestions system is one of the new cooperative management plans, which affects making good decisions, self-confidence and employees occupational approval. The aim of this research is to survey the barriers for establishment of suggestion system. The proposed study designs and distributes a questionnaire among 195 out of 26550 managers who work for educational system in city of Kermanshah, Iran. The primary purpose of this survey is to learn whether lack of accepting any risk on behalf of management team, conflict of interest in management style, lack of belief on suggestions system in an organization are important barriers of executing suggestions system in an organization or not. The results of Freedman test confirm all seven components of the hypothesis. As a result, lack of accepting risk among management team is number one barrier in having suggestion system followed by existing conflict between management style and suggestions system and lack of management's belief to suggestions system, weakness in education for suggestions system and fear in management disruption because of having suggestions system. The other barriers coming in the last priority in terms of their relative importance include lack of management's support to suggestions system and weakness in management position because of accepting suggestions system.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".