Designing a performance appraisal system based on balanced scorecard for improving productivity: Case study in Semnan technology and science park
Bibliographic record
Abstract
Today, organizations for holding and improving their competing merit use performance measurement for evaluation, control, supervision and improvement of their trading processes.Medium and small companies in technology and science parks are very useful in economic revivification and technology development.Technology and science parks have provided necessary consultations, information, suitable equipments, and services for developing technology unites and prepare them for independent presence in industry.One of the necessary elements for the success and improvement of performance in these companies is to establish and implement balanced scorecard, which can be used to reach desired goals, strategies and to improve performance.In this article, we use a structured method for calculating efficiency of four perspectives of balanced scorecard.Statistical society of this research was Semnan technology and Science Park and seven experts are selected for answering questions of the survey.We also complete questionnaire and determine index and relative importance of all indices.For developing strategic goals of Semnan technology and science park according to four perspectives of balanced score card (finance, growth and learning, internal process), six meetings were hold and finally all crisis macro goals index were identified and they were analyzed for evaluating performance.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".