Bibliographic record
Abstract
The purpose of this research is to identify and prioritize factors affecting organizational performance evaluation using analytic hierarchical process and balanced scorecard in Social Security Organization of Khuzestan Province.The methodology used in this research in terms of purpose is applieddevelopmental and in terms of nature is descriptive-survey.The statistical population of this research includes the senior managers, master experts and managers of branches of Social Security Organization of Khuzestan Province.The size of population is equal to 15.The tools used in this research include an authormade questionnaire, Delphi technique and analytic hierarchical process (AHP).Data analysis is done using expert choice software.According to the results, financial perspective with a weight of 0.412 was the first priority and customer perspective, internal processes, and growth and learning with weights of 0.383, 0.126 and 0.079, respectively, were the next priorities.To obtain the final weight of each of the sub-indicators, the perspective weight should be multiplied by its indicator.This way, the final weights are obtained, which reducing cost from a financial perspective, customer satisfaction and creating a positive image in the customer's mind from the customer perspective have, in order, the highest importance and priority from perspective of the experts of this research.On the other hand, the use of technology, the cost of R&D, and organizational climate change have the least priority.The important point is that all of these three subindicators are considered from the learning perspective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.936 | 0.924 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".