A development in balanced scorecard by designing a fuzzy and nonlinear Algorithm (case study: Islamic Azad university of Semnan)
Bibliographic record
Abstract
The success of each organization depends undoubtedly on the quality of its management and management quality depends on decision quality and information quality on the quality of its measurement and proportion.Therefore, its accuracy and measurement has a key role in the success of the organization and the weakness of performance evaluation and managerial control system can transfer to a barrier for the growth of organization.Performance evaluation systems are now dividable to two traditional group (performance evaluation of an individual across reminding him about his performance) and modern group (developing and improving the capacity of evaluated individual and inclined to achievement of organizational objectives and strategies).One of the most authoritative strategic models in this field is the balanced scorecard (BSC) model in which entire aspects of an organization are dominantly investigated.However, no operational trend has been introduced for utilizing it up to now.In this paper, an operational trend is introduced to apply the foundations of BSC model and multiple criteria decision making (MCDM) techniques.The most important goal of researchers in representation of new structure for creating development and growth capacity and permanent improvement is associated by a kind of providence, such that it can develop desirable organizational and work behaviors towards achieving the objectives and strategies of the organization.In addition, the strategic planning of Islamic Azad university of Semnan was modeled by suggested structure to validate the suggested structure's capacities.The results showed that outputs were more tangible for the personnel of the organization and the results were accepted by the managers of Islamic Azad university of Semnan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".